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GarzaFabre, Mario; RodriguezTello, Eduardo; ToscanoPulido, Gregorio
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2 Citations
Through multiobjectivization, a singleobjective problem is restated in multiobjective form with the aim of enabling a more efficient search process. Recently, this transformation was applied with success to the hydrophobicpolar (HP) lattice model, which is an abstract representation of the protein structure prediction problem. The use of alternative multiobjective formulations of the problem has led to significantly better results. In this paper, an improved multiobjectivization for the HP model is proposed. By decomposing the HP model’s energy function, a twoobjective formulation for the problem is defined. A comparative analysis reveals that the new proposed multiobjectivization evaluates favorably with respect to both the conventional singleobjective and the previously reported multiobjective formulations. Statistical significance testing and the use of a large set of test cases support the findings of this study. Both twodimensional and threedimensional lattices are considered.
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GarzaFabre, Mario; RodriguezTello, Eduardo; ToscanoPulido, Gregorio
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3 Citations
The hydrophobicpolar (HP) model for protein structure prediction abstracts the fact that hydrophobic interactions are a dominant force in the protein folding process. This model represents a hard combinatorial optimization problem, which has been widely addressed using evolutionary algorithms and other metaheuristics. In this paper, the multiobjectivization of the HP model is proposed. This originally singleobjective problem is restated as a multiobjective one by decomposing the conventional objective function into two independent objectives. By using different evolutionary algorithms and a large set of test cases, the new alternative formulation was compared against the conventional singleobjective problem formulation. As a result, the proposed formulation increased the search performance of the implemented algorithms in most of the cases. Both two and threedimensional lattices are considered. To the best of authors’ knowledge, this is the first study where multiobjective optimization methods are used for solving the HP model.
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Pierrard, Thomas; Coello Coello, Carlos A.
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3 Citations
This paper presents a new artificial immune system algorithm for solving multiobjective optimization problems, based on the clonal selection principle and the hypervolume contribution. The main aim of this work is to investigate the performance of this class of algorithm with respect to approaches which are representative of the stateoftheart in multiobjective optimization using metaheuristics. The results obtained by our proposed approach, called multiobjective artificial immune system based on hypervolume (MOAISHV) are compared with respect to those of the NSGAII. Our preliminary results indicate that our proposed approach is very competitive, and can be a viable choice for solving multiobjective optimization problems.
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Hannachi, Mohamed Amine; Bouassida Rodriguez, Ismael; Drira, Khalil; Pomares Hernandez, Saul Eduardo
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2 Citations
Multilabelled graphs are a powerful and versatile tool for modelling real applications in diverse domains such as communication networks, social networks, and autonomic systems, among others. Due to dynamic nature of such kind of systems the structure of entities is continuously changing along the time, this because, it is possible that new entities join the system, some of them leave it or simply because the entities’ relations change. Here is where graph transformation takes an important role in order to model systems with dynamic and/or evolutive configurations. Graph transformation consists of two main tasks: graph matching and graph rewriting. At present, few graph transformation tools support multilabelled graphs. To our knowledge, there is no tool that support inexact graph matching for the purpose of graph transformation. Also, the main problem of these tools lies on the limited expressiveness of rewriting rules used, that negatively reduces the range of application scenarios to be modelling and/or negatively increase the number of rewriting rules to be used. In this paper, we present the tool GMTE  Graph Matching and Transformation Engine. GMTE handles directed and multilabelled graphs. In addition, to the exact graph matching, GMTE handles the inexact graph matching. The approach of rewriting rules used by GMTE combines Single PushOut rewriting rules with edNCE grammar. This combination enriches and extends the expressiveness of the graph rewriting rules. In addition, for the graph matching, GMTE uses a conditional rule schemata that supports complex comparison functions over labels. To our knowledge, GMTE is the first graph transformation tool that offers such capabilities.
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Ita, Guillermo; Bello, Pedro; Contreras, Meliza
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To count models for two conjunctive forms (#2SAT problem) is a classic #P problem. We determine different structural patterns on the underlying graph of a 2CF F allowing the efficient computation of #2SAT(F).
We show that if the constrained graph of a formula is acyclic or the cycles on the graph can be arranged as independent and embedded cycles, then the number of models of F can be counted efficiently.
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AlbaCabrera, Eduardo; IbarraFiallo, Julio; GodoyCalderon, Salvador; CervantesAlonso, Fernando
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The last few years have seen an important increase in research publications dealing with external typical testorfinding algorithms, while internal ones have been almost forgotten or modified to behave as external on the basis of their alleged poor performance. In this research we present a new internal typical testorfinding algorithm called YYC that incrementally calculates typical testors for the currently analized set of basic matrix rows by searching for compatible sets. The experimentally measured performance of this algorithm stands out favorably in problems where other external algorithms show very low performance. Also, a comparative analysis of its efficiency is done against some external typical testorfinding algorithms published during the last few years.
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Rendón, Erendira; Sánchez, José Salvador
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1 Citations
Clustering in data mining is a discovery process that groups a set of data so as to maximize the intracluster similarity and to minimize the intercluster similarity. Clustering becomes more challenging when data are categorical and the amount of available memory is less than the size of the data set. In this paper, we introduce CBC (Clustering Based on Compressed Data), an extension of the Birch algorithm whose main characteristics refer to the fact that it can be especially suitable for very large databases and it can work both with categorical attributes and mixed features. Effectiveness and performance of the CBC procedure were compared with those of the wellknown Kmodes clustering algorithm, demonstrating that the CBC summary process does not affect the final clustering, while execution times can be drastically lessened.
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García, Vicente; Mollineda, Ramón A.; Sánchez, J. Salvador
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2 Citations
In this paper, we introduce a new approach to evaluate and visualize the classifier performance in twoclass imbalanced domains. This method defines a twodimensional space by combining the geometric mean of class accuracies and a new metric that gives an indication of how balanced they are. A given point in this space represents a certain tradeoff between those two measures, which will be expressed as a trapezoidal function. Besides, this evaluation function has the interesting property that it allows to emphasize the correct predictions on the minority class, which is often considered as the most important class. Experiments demonstrate the consistency and validity of the evaluation method here proposed.
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SolorioFernández, Saúl; CarrascoOchoa, J. Ariel; MartínezTrinidad, José Fco.
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4 Citations
In this paper, we introduce a new hybrid filterwrapper method for supervised feature selection, based on the Laplacian Score ranking combined with a wrapper strategy. We propose to rank features with the Laplacian Score to reduce the search space, and then we use this order to find the best feature subset. We compare our method against other based on ranking feature selection methods, namely, Information Gain Attribute Ranking, Relief, Correlationbased Feature Selection, and additionally we include in our comparison a Wrapper Subset Evaluation method. Empirical results over ten realworld datasets from the UCI repository show that our hybrid method is competitive and outperforms in most of the cases to the other feature selection methods used in our experiments.
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Hernández Gómez, Raquel; Coello Coello, Carlos A.; Alba, Enrique
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2 Citations
In the last decade, there has been a growing interest in multiobjective evolutionary algorithms that use performance indicators to guide the search. A simple and effective one is the
$$\mathcal {S}$$
Metric Selection Evolutionary MultiObjective Algorithm (SMSEMOA), which is based on the hypervolume indicator. Even though the maximization of the hypervolume is equivalent to achieving Pareto optimality, its computational cost increases exponentially with the number of objectives, which severely limits its applicability to manyobjective optimization problems. In this paper, we present a parallel version of SMSEMOA, where the execution time is reduced through an asynchronous island model with micropopulations, and diversity is preserved by external archives that are pruned to a fixed size employing a recently created technique based on the ParallelCoordinates graph. The proposed approach, called
$$\mathcal {S}$$
PAMICRO (PArallel MICRo Optimizer based on the
$$\mathcal {S}$$
metric), is compared to the original SMSEMOA and another stateoftheart algorithm (HypE) on the WFG test problems using up to 10 objectives. Our experimental results show that
$$\mathcal {S}$$
PAMICRO is a promising alternative that can solve manyobjective optimization problems at an affordable computational cost.
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SantiagoRamirez, Everardo; GonzalezFraga, J. A.; AscencioLopez, J. I.
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2 Citations
In this paper, we compare the performance of three composite correlation filters in facial recognition problem. We used the ORL (Olivetti Research Laboratory) facial image database to evaluate KLaw, MACE and ASEF filters performance. Simulations results demonstrate that KLaw nonlinear composite filters evidence the best performance in terms of recognition rate (RR) and, false acceptation rate (FAR). As a result, we observe that correlation filters are able to work well even when the facial image contains distortions such as rotation, partial occlusion and different illumination conditions.
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GarcíaValdez, Mario; Guervós, Juan Julián Merelo; Fernández de Vega, Francisco
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In this paper the effect of node unavailability in algorithms using EvoSpace, a poolbased evolutionary algorithm, is assessed. EvoSpace is a framework for developing evolutionary algorithms (EAs) using heterogeneous and unreliable resources. It is based on Linda’s tuple space coordination model. The core elements of EvoSpace are a central repository for the evolving population and remote clients, here called EvoWorkers, which pull random samples of the population to perform on them the basic evolutionary processes (selection, variation and survival), once the work is done, the modified sample is pushed back to the central population. To address the problem of unreliable EvoWorkers, EvoSpace uses a simple reinsertion algorithm using copies of samples stored in a global queue which also prevents the starvation of the population pool. Using a benchmark problem from the PPeaks problem generator we have compared two approaches: (i) the reinsertion of previous individuals at the cost of keeping copies of each sample, and a common approach of other pool based EAs, (ii) inserting randomly generated individuals. We found that EvoSpace is fault tolerant to highly unreliable resources and also that the reinsertion algorithm is only needed when the population is near the point of starvation.
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Acevedo, Elena; Acevedo, Antonio; Felipe, Federico
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2 Citations
A method for diagnosing Parkinson’s disease is presented. The proposal is based on associative approach, and we used this method for classifying patients with Parkinson’s disease and those who are completely healthy. In particular, AlphaBeta Bidirectional Associative Memory is used together with the modified JohnsonMöbius codification in order to deal with mixed noise. We use three methods for testing the performance of our method: LeaveOneOut, HoldOut and Kfold Cross Validation and the average obtained was of 97.17%.
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CruzBarbosa, Raúl; BautistaVillavicencio, David; Vellido, Alfredo
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The diagnostic classification of human brain tumours on the basis of magnetic resonance spectra is a nontrivial problem in which dimensionality reduction is almost mandatory. This may take the form of feature selection or feature extraction. In feature extraction using manifold learning models, multivariate data are described through a lowdimensional manifold embedded in data space. Similarities between points along this manifold are best expressed as geodesic distances or their approximations. These approximations can be computationally intensive, and several alternative software implementations have been recently compared in terms of computation times. The current brief paper extends this research to investigate the comparative ability of dimensionalityreduced data descriptions to accurately classify several types of human brain tumours. The results suggest that the way in which the underlying data manifold is constructed in nonlinear dimensionality reduction methods strongly influences the classification results.
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PérezAlfonso, Damián; FundoraRamírez, Osiel; LazoCortés, Manuel S.; RocheEscobar, Raciel
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Process mining is concerned with the extraction of knowledge about business processes from information system logs. Process discovery algorithms are process mining techniques focused on discovering process models starting from event logs. The applicability and effectiveness of process discovery algorithms rely on features of event logs and process characteristics. Selecting a suitable algorithm for an event log is a tough task due to the variety of variables involved in this process. The traditional approaches use empirical assessment in order to recommend a suitable discovery algorithm. This is a time consuming and computationally expensive approach. The present paper evaluates the usefulness of an approach based on classification to recommend discovery algorithms. A knowledge base was constructed, based on features of event logs and process characteristics, in order to train the classifiers. Experimental results obtained with the classifiers evidence the usefulness of the proposal for recommendation of discovery algorithms.
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Arias Montaño, Alfredo; Coello Coello, Carlos A.; MezuraMontes, Efrén
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This paper introduces a novel Parallel MultiObjective Evolutionary Algorithm (pMOEA) which is based on the island model. The serial algorithm on which this approach is based uses the differential evolution operators as its search engine, and includes two mechanisms for improving its convergence properties (through local dominance and environmental selection based on scalar functions). Two different parallel approaches are presented. The first aims at improving effectiveness (i.e., for better approximating the Pareto front) while the second aims to provide a better efficiency (i.e., by reducing the execution time through the use of small population sizes in each subpopulation). To assess the performance of the proposed algorithms, we adopt a set of standard test functions and performance measures taken from the specialized literature. Results are compared with respect to its serial counterpart and with respect to three algorithms representative of the stateoftheart in the area: NSGAII, MOEA/D and MOEA/DDE.
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GarcíaHernández, René Arnulfo; Ledeneva, Yulia
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4 Citations
Extractive text summarization consists in selecting the most important units (normally sentences) from the original text, but it must be done as closer as humans do. Several interesting automatic approaches are proposed for this task, but some of them are focused on getting a better result rather than giving some assumptions about what humans use when producing a summary. In this research, not only the competitive results are obtained but also some assumptions are given about what humans tried to represent in a summary. To reach this objective a genetic algorithm is proposed with special emphasis on the fitness function which permits to contribute with some conclusions.
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VillatoroTello, Esaú; VillaseñorPineda, Luis; MontesyGómez, Manuel
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1 Citations
Recent evaluation results from Geographic Information Retrieval (GIR) indicate that current information retrieval methods are effective to retrieve relevant documents for geographic queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results in this paper we present a novel reranking method, which employs information obtained through a relevance feedback process to perform a ranking refinement. Performed experiments show that the proposed method allows to improve the generated ranking from a traditional IR machine, as well as results from traditional reranking strategies such as query expansion via relevance feedback.
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Coello Coello, Carlos A.
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3 Citations
This paper provides a brief introduction to the socalled multiobjective evolutionary algorithms, which are bioinspired metaheuristics designed to deal with problems having two or more (normally conflicting) objectives. First, we provide some basic concepts related to multiobjective optimization and a brief review of approaches available in the specialized literature. Then, we provide a short review of applications of multiobjective evolutionary algorithms in pattern recognition. In the final part of the paper, we provide some possible paths for future research in this area, which are promising, from the author’s perspective.
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Gelbukh, Alexander; Sidorov, Grigori
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Parallel text alignment is a special type of pattern recognition task aimed to discover the similarity between two sequences of symbols. Given the same text in two different languages, the task is to decide which elements—paragraphs in case of paragraph alignment—in one text are translations of which elements of the other text. One of the applications is training training statistical machine translation algorithms. The task is not trivial unless detailed text understanding can be afforded. In our previous work we have presented a simple technique that relied on bilingual dictionaries but does not perform any syntactic analysis of the texts. In this paper we give a formal definition of the task and present an exact optimization algorithm for finding the best alignment.
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Peña Ayala, Alejandro; Sossa, Humbero
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In this paper an approach oriented to acquire, depict, and administrate knowledge about the student is proposed. Moreover, content is also characterized to describe lectures. In addition, the work focuses on the semantics of the attributes that reveal a profile of the student and the teaching experiences. The meaning of such properties is stated as an ontology. Thus, inheritance and causal inferences are made. According to the semantics of the attributes and the conclusions induced, the sequencing module of a Webbased educational system (WBES) delivers the appropriate option of lecture to students. The underlying hypothesis is: the apprenticeship of students is enhanced when a WBES understands the nature of the content and the student’s characteristics. Based on the empirical evidence outcome by a trial, it is concluded that: Successful WBES account the knowledge that describe their students and lectures.
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Ritter, Gerhard X.; Urcid, Gonzalo
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This paper presents an overview of the current status of lattice based dendritic computing. Roughly speaking, lattice based dendritic computing refers to a biomimetic approach to artificial neural networks whose computational aspects are based on lattice group operations. We begin our presentation by discussing some important processes of biological neurons followed by a biomimetic model which implements these processes. We discuss the reasons and rationale behind this approach and illustrate the methodology with some examples. Global activities in this field as well as some potential research issues are also part of this discussion.
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OlveraLópez, J. Arturo; MartínezTrinidad, J. Francisco; CarrascoOchoa, J. Ariel
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3 Citations
The object selection is an important task for instancebased classifiers since through this process the size of a training set could be reduced and then the runtimes in both classification and training steps would be reduced. Several methods for object selection have been proposed but some methods discard relevant objects for the classification step. In this paper, we propose an object selection method which is based on the idea of sequential floating search. This method reconsiders the inclusion of relevant objects previously discarded. Some experimental results obtained by our method are shown and compared against some other object selection methods.
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Oca, Víctor Montes; Torres, Miguel; Levachkine, Serguei; Moreno, Marco
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In this paper, we propose the use of a knowledge based system, which has been implemented in SWIProlog to approach the automatic description of spatial data by means of some logic rules. The process to establish the predicates is based on the topological and geometrical analysis of spatial data. These predicates are handled by a set of rules, which are used to find the relations between geospatial objects. Moreover, the rules aid the searching of several features that compose the partition of topographic maps. For instance, in the case that any road intersects with other, we appreciate that a connection relation exists between different destinies, which can be accessed by these roads. Furthermore, the rules help us to know each possible access for this case. Therefore, this description assists in the tasks of geospatial data interpretation (map description) in order to provide quality information for spatial decision support systems.
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Cleofas, Laura; Valdovinos, Rosa Maria; García, Vicente; Alejo, Roberto
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2 Citations
In realworld applications, it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. One method to tackle this problem consists to resample the original training set, either by oversampling the minority class and/or undersampling the majority class. In this paper, we propose two ensemble models (using a modular neural network and the nearest neighbor rule) trained on datasets undersampled with genetic algorithms. Experiments with real datasets demonstrate the effectiveness of the methodology here proposed.
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Batyrshin, Ildar; Solovyev, Valery
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The paper introduces new time series shape association measures based on Euclidean distance. The method of analysis of associations between time series based on separate analysis of positively and negatively associated local trends is discussed. The examples of application of the proposed measures and methods to analysis of associations between historical prices of securities obtained from Google Finance are considered. An example of time series with inverse associations between them is discussed.
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DomínguezMedina, Christian; CruzCortés, Nareli
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Wireless Sensor Networks have become an active research topic in the last years. The routing problem is a very important part in this kind of networks that need to be considered in order to maximize the network life time. As the size of the network increases, routing becomes more complex due the amount of sensor nodes in the network. Sensor nodes in Wireless Sensor Networks are very constrained in memory capabilities, processing power and batteries. Ant Colony Optimization based routing algorithms have been proposed to solve the routing problem trying to deal with these constrains. We present a comparison of two Ant Colonybased routing algorithms, taking into account current amounts of energy consumption under different scenarios and reporting the usual metrics for routing in wireless sensor networks.
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AguilarGonzález, Pablo M.; Kober, Vitaly
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Correlation filters for object detection and location estimation are commonly designed assuming the shape and graylevel structure of the object of interest are explicitly available. In this work we propose the design of correlation filters when the appearance of the target is given in a single training image. The target is assumed to be embedded in a cluttered background and the image is assumed to be corrupted by additive sensor noise. The designed filters are used to detect the target in an input scene modeled by the nonoverlapping signal model. An optimal correlation filter, with respect to the peaktooutput energy ratio criterion, is proposed for object detection and location estimation. We also present estimation techniques for the required parameters. Computer simulation results obtained with the proposed filters are presented and compared with those of common correlation filters.
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HernándezRodríguez, Selene; CarrascoOchoa, J. A.; MartínezTrinidad, J. Fco.
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The k nearest neighbor (kNN) classifier has been extensively used as a nonparametric technique in Pattern Recognition. However, in some applications where the training set is large, the exhaustive kNN classifier becomes impractical. Therefore, many fast kNN classifiers have been developed to avoid this problem. Most of these classifiers rely on metric properties, usually the triangle inequality, to reduce the number of prototype comparisons. However, in soft sciences, the prototypes are usually described by qualitative and quantitative features (mixed data), and sometimes the comparison function does not satisfy the triangle inequality. Therefore, in this work, a fast k most similar neighbor (kMSN) classifier, which uses a Tree structure and an Approximating and Eliminating approach for Mixed Data, not based on metric properties (Tree AEMD), is introduced. The proposed classifier is compared against other fast kNN classifiers.
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Jaimes, Antonio López; Aguirre, Hernán; Tanaka, Kiyoshi; Coello Coello, Carlos A.
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Here, we present a partition strategy to generate objective subspaces based on the analysis of the conflict information obtained from the Pareto front approximation found by an underlying multiobjective evolutionary algorithm. By grouping objectives in terms of the conflict among them, we aim to separate the multiobjective optimization into several subproblems in such a way that each of them contains the information to preserve as much as possible the structure of the original problem. The ranking and parent selection is independently performed in each subspace. Our experimental results show that the proposed conflictbased partition strategy outperforms NSGAII in all the test problems considered in this study. In problems in which the degree of conflict among the objectives is significantly different, the conflictbased strategy achieves its best performance.
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KuriMorales, Angel; CortésArce, Iván
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Computer Networks are usually balanced appealing to personal experience and heuristics, without taking advantage of the behavioral patterns embedded in their operation. In this work we report the application of tools of computational intelligence to find such patterns and take advantage of them to improve the network’s performance. The traditional traffic flow for Computer Network is improved by the concatenated use of the following “tools”: a) Applying intelligent agents, b) Forecasting the traffic flow of the network via MultiLayer Perceptrons (MLP) and c) Optimizing the forecasted network’s parameters with a genetic algorithm. We discuss the implementation and experimentally show that every consecutive new tool introduced improves the behavior of the network. This incremental improvement can be explained from the characterization of the network’s dynamics as a set of emerging patterns in time.
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RosalesPérez, Alejandro; ReyesGarcía, Carlos A.; Gonzalez, Jesus A.; ArchTirado, Emilio
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In the last years, infant cry recognition has been of particular interest because it contains useful information to determine if the infant is hungry, has pain, or a particular disease. Several studies have been performed in order to differentiate between these kinds of cries. In this work, we propose to use Genetic Selection of a Fuzzy Model (GSFM) for classification of infant cry. GSFM selects a combination of feature selection methods, type of fuzzy processing, learning algorithm, and its associated parameters that best fit to the data. The experiments demonstrate the feasibility of this technique in the classification task. Our experimental results reach up to 99.42% accuracy.
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Rubio, Juan Pablo Serrano; Aguirre, Arturo Hernández; Guzmán, Rafael Herrera
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This paper introduces an Evolutionary Algorithm in Conformal Space (EACS) for global continuous optimization and its implementation by using Conformal Geometric Algebra (CGA). Two new geometric search operators are included in the design of the EACS: Inversion Search Operator (ISO) and Reflection Search Operator (RSO). The ISO computes the inverse points with respect to hyperspheres, and the RSO redistributes the individuals on the surface of the hypersphere. The nonlinear geometric nature of the ISO furnishes and enhances the search capability of the algorithm. The reproduction operators are described in the framework of the CGA. CGA provides a concise way to perform rigid euclidean transformations(rotations, translations, reflections) and inversions on hyperspheres. These transformations are easily computed by using the products of the CGA. The performance of the EACS is analyzed through a benchmark of 28 functions. Statistical tests show the competitive performance of EACS in comparison with current leading algorithms (PSO and DE).
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CoelloCoello, Carlos A.
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In this paper, we provide a general introduction to the socalled multiobjective evolutionary algorithms, which are metaheuristic search techniques inspired on natural evolution that are able to deal with highly complex optimization problems having two or more objectives. In the first part of the paper, we provide some basic concepts necessary to make the paper selfcontained, as well as a short review of the most representative multiobjective evolutionary algorithms currently available in the specialized literature. After that, a short review of applications of these algorithms in pattern recognition is provided. The final part of the paper presents some possible future research paths in this area as well as our conclusions.
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Fraga, Luis Gerardo; Coello Coello, Carlos A.
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2 Citations
This chapter presents a review of some of the most representative work regarding techniques and applications of evolutionary algorithms in pattern recognition. Evolutionary algorithms are a set of metaheuristics inspired on Darwins “survival of the fittest” principle which are stochastic in nature. Evolutionary algorithms present several advantages over traditional search and classification techniques, since they require less domainspecific information, are easy to use and operate on a set of solutions (the socalled population). Such advantages have made them very popular within pattern recognition (as well as in other domains) as will be seen in the review of applications presented in this chapter.
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Sossa, Humberto; Garro, Beatriz A.; Villegas, Juan; Avilés, Carlos; Olague, Gustavo
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In this note we present our most recent advances in the automatic design of artificial neural networks (ANNs) and associative memories (AMs) for pattern classification and pattern recall. Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithms are used for ANNs; Genetic Programming is adopted for AMs. The derived ANNs and AMs are tested with several examples of wellknown databases. As we will show, results are very promising.
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Villalobos Castaldi, Fabiola M.; FelipeRiveron, Edgardo M.; Gómez, Ernesto Suaste
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The retinal vascular network has many desirable characteristics as a basis for authentication, including uniqueness, stability, and permanence. In this paper, a new approach for retinal images features extraction and template coding is proposed. The use of the logarithmic spiral sampling grid in scanning and tracking the vascular network is the key to make this new approach simple, flexible and reliable. Experiments show that this approach can achieve the reduction of data dimensionality and of the required time to obtain the biometric code of the vascular network in a retinal image. The performed experiments demonstrated that the proposed verification system has an average accuracy of 95.0 – 98 %.
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Suaste, Verónica; Caudillo, Diego; Shin, BokSuk; Klette, Reinhard
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Thirdeye stereo analysis evaluation compares a virtual image, derived from results obtained by binocular stereo analysis, with a recorded image at the same pose. This technique is applied for evaluating stereo matchers on long (or continuous) stereo input sequences where no ground truth is available. The paper provides a critical and constructive discussion of this method. The paper also introduces data measures on input video sequences as an additional tool for analyzing issues of stereo matchers occurring for particular scenarios. The paper also reports on extensive experiments using two toprated stereo matchers.
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GarciaNajera, Abel
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2 Citations
In the Vehicle Routing Problem with Backhauls there are linehaul customers, who demand products, and backhaul customers, who supply products, and there is a fleet of vehicles available for servicing customers. The problem consists in finding a set of routes with the minimum cost, such that all customers are serviced. A generalization of this problem considers the collection from the backhaul customers optional. If the number of vehicles, the cost, and the uncollected demand are assumed to be equally important objectives, the problem can be tackled as a multiobjective optimization problem. In this paper, we solve these as multiobjective problems with an adapted previously proposed evolutionary algorithm and evaluate its performance with proper tools.
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JiménezGuarneros, Magdiel; CarrascoOchoa, Jesús Ariel; MartínezTrinidad, José Fco.
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Currently, graph embedding has taken a great interest in the area of structural pattern recognition, especially techniques based on representation via dissimilarity. However, one of the main problems of this technique is the selection of a suitable set of prototype graphs that better describes the whole set of graphs. In this paper, we evaluate the use of an instance selection method based on clustering for graph embedding, which selects border prototypes and some nonborder prototypes. An experimental evaluation shows that the selected method gets competitive accuracy and better runtimes than other state of the art methods.
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Graff, Mario; Poli, Riccardo
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1 Citations
Most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. In this paper, two models of evolutionary programinduction algorithms (EPAs) are proposed which overcome this limitation. We test our approach with two important classes of problems — symbolic regression and Boolean function induction — and a variety of EPAs including: different versions of genetic programming, gene expression programing, stochastic iterated hill climbing in program space and one version of cartesian genetic programming. We compare the proposed models against a practical model of EPAs we previously developed and find that in most cases the new models are simpler and produce better predictions. A great deal can also be learnt about an EPA via a simple inspection of our new models. E.g., it is possible to infer which characteristics make a problem difficult or easy for the EPA.
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GarcíaValdez, Mario; Trujillo, Leonardo; MereloGuérvos, Juan Julián; FernándezdeVega, Francisco
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4 Citations
Recently, several Poolbased Evolutionary Algorithms (PEAs) have been proposed, that asynchronously distribute an evolutionary search among heterogeneous devices, using controlled nodes and nodes outside the local network, through web browsers or cloud services. In PEAs, the population is stored in a shared pool, while distributed processes called workers execute the actual evolutionary search. This approach allows researchers to use low cost computational power that might not be available otherwise. On the other hand, it introduces the challenge of leveraging the computing power of heterogeneous and unreliable resources. The heterogeneity of the system suggests that using a heterogeneous parametrization might be a better option, so the goal of this work is to test such a scheme. In particular, this paper evaluates the strategy proposed by Gong and Fukunaga for the IslandModel, which assigns random control parameter values to each worker. Experiments were conducted to assess the viability of this strategy on poolbased EAs using benchmark problems and the EvoSpace framework. The results suggest that the approach can yield results which are competitive with other parametrization approaches, without requiring any form of experimental tuning.
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MenchacaMendez, Adriana; Coello Coello, Carlos A.
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In this paper, we propose an approach that combines a modified version of the maximin fitness function and the hypervolume indicator for selecting individuals into a MultiObjective Evolutionary Algorithm (MOEA). Our proposed selection mechanism is incorporated into a MOEA which adopts the crossover and mutation operators of the Nondominated Sorting Genetic AlgorithmII (NSGAII), giving rise to the socalled “MaximinHypervolume MultiObjective Evolutionary Algorithm (MHMOEA)”. Our proposed MHMOEA is validated using standard test problems taken from the specialized literature, using from three to six objectives. Our results are compared with respect to those produced by MCMOEA (which is based on the maximin fitness function and a clustering technique), MOEA/D using Penalty Boundary Intersection (PBI), which is based on decomposition and iSMSEMOA (which is based on the hypervolume indicator). Our preliminary results indicate that our proposed MHMOEA is a good alternative to solve multiobjective optimization problems having both low dimensionality and high dimensionality in objective function space.
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LeboeufPasquier, Jérôme
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The purpose of this paper is to exhibit the process of a Growing Functional Modules (GFM) controller designed for a humanoid robot balance learning. This learning based controller is graphically generated by interconnecting and configuring four kinds of components: Global Goals, Acting Modules, Sensing Modules and Sensations. Global Goals specify the intrinsic motivations of the controller. Acting and Sensing Modules develop their acting and respectively, sensing functionalities while interacting with the environment. Sensations provide the controlled system’s feedback that renders the effects produced by the previous command. These characteristics together with an endless learning process allow the controller to perform as an ‘artificial brain’. The present paper describes the design, functioning and performance of a humanoid equilibrium subsystem that learns balancing the robot on one foot meanwhile a disequilibrium is artificially produced by moving the opposite leg.
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Alba, Alfonso; ArceSantana, Edgar
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In this paper, we propose a new theoretical framework, which is based on phasecorrelation, for efficiently solving the correspondence problem. The proposed method allows area matching algorithms to perform at high frame rates, and can be applied to various problems in computer vision. In particular, we demonstrate the advantages of this method in the estimation of dense disparity maps in real time. A fairly optimized version of the proposed algorithm, implemented on a dualcore PC architecture, is capable of running at 100 frames per second with an image size of 256 ×256.
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LazoCortés, Manuel S.; MartínezTrinidad, José Francisco; CarrascoOchoa, Jesús Ariel
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In this article, we revisit the concept of Goldman’s fuzzy testor and restudy it from the perspective of the conceptual approach to attribute reduct introduced by Y.Y. Yao in the framework of Rough Set Theory. We reformulate the original concept of Goldman’s fuzzy testor and we introduce the Goldman’s fuzzy reducts. Additionally, in order to show the usefulness of the Goldman’s fuzzy reducts, we build a rule based classifier and evaluate its performance in a case of study.
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OrtizBayliss, José Carlos; TerashimaMarín, Hugo; ConantPablos, Santiago Enrique
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Hyperheuristics are methodologies that choose from a set of heuristics and decide which one to apply given some properties of the current instance. When solving a constraint satisfaction problem, the order in which the variables are selected to be instantiated has implications in the complexity of the search. In this paper we propose a logistic regression model to generate hyperheuristics for variable ordering within constraint satisfaction problems. The first step in our approach requires to generate a training set that maps any given instance, expressed in terms of some of their features, to one suitable variable ordering heuristic. This set is used later to train the system and generate a hyperheuristic that decides which heuristic to apply given the current features of the instances at hand at different steps of the search. The results suggest that hyperheuristics generated through this methodology allow us to exploit the strengths of the heuristics to minimize the cost of the search.
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Monroy, Raúl
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In the formal methods approach to software verification, we use logical formulae to model both the program and its intended specification, and, then, we apply (automated) reasoning techniques to demonstrate that the formulae satisfy a verification conjecture. One may either apply proving techniques, to provide a formal verification argument, or disproving techniques to falsify the verification conjecture. However, programs often contain bugs or are flawed, and, so, the verification process breaks down. Interpreting the failed proof attempt or the counterexample, if any, is very valuable, since it potentially helps identifying the program bug or flaw. Lakatos, in his book Proofs and Refutations, argues that the analysis of a failed proof often holds the key for the development of a theory. Proof analysis enables the strengthening of naïve conjectures and concepts, without severely weakening its content. In this paper, we survey our encounters on the productive use of failure in the context of a few theories, natural numbers and (higherorder) lists, and in the context of security protocols.
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GonzálezGómez, Efrén; Levachkine, Serguei
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Hard problem of cartographic pattern recognition in fine scale maps, using information that comes from coarse scale maps, is considered. The maps are rasterscanned color maps of different thematic, representing the same territory in coarse and fine scale respectively. A solution called CoarsetoFine Scale Method is proposed. This method is defined in terms of means: coarse scale maps and their information; concepts: image associated function, cartographic knowledge domain and cartographic pattern; and tools: a set of clustering criteria of the Logical Combinatorial Pattern Recognition.
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Zapotecas Martínez, Saúl; Yáñez Oropeza, Edgar G.; Coello Coello, Carlos A.
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In spite of the success of evolutionary algorithms for dealing with multiobjective optimization problems (the socalled multiobjective evolutionary algorithms (MOEAs)), their main drawback is the finetuning of their parameters, which is normally done in an empirical way (using a trialanderror process for each problem at hand), and usually has a significant impact on their performance. In this paper, we present a selfadaptation methodology that can be incorporated into any MOEA, in order to allow an automatic finetuning of parameters, without any human intervention. In order to validate the proposed mechanism, we incorporate it into the NSGAII, which is a wellknown elitist MOEA and we analyze the performance of the resulting approach. The results reported here indicate that the proposed approach is a viable alternative to selfadapt the parameters of a MOEA.
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MenchacaMendez, Adriana; Montero, Elizabeth; Riff, MaríaCristina; Coello, Carlos A. Coello
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In this paper, we study iSMSEMOA, a recently proposed approach that improves the wellknown S metric selection Evolutionary MultiObjective Algorithm (SMSEMOA). These two indicatorbased multiobjective evolutionary algorithms rely on hypervolume contributions to select individuals. Here, we propose to define a probability of using a randomly selected individual within the iSMSEMOA’s selection scheme. In order to calibrate the value of such probability, we use the EVOCA tuner. Our preliminary results indicate that we are able to save up to 33% of computations of the contribution to hypervolume with respect to the original iSMSEMOA, without any significant quality degradation in the solutions obtained. In fact, in some cases, the approach proposed here was even able to improve the quality of the solutions obtained by the original iSMSEMOA.
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Gaxiola, Fernando; Melin, Patricia; Valdez, Fevrier; Castillo, Oscar
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This paper presents a new modular neural network architecture that is used to build a system for pattern recognition based on the iris biometric measurement of persons. In this system, the properties of the person iris database are enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The inputs to the modular neural network are the processed iris images and the output is the number of the identified person. The integration of the modules was done with a type2 fuzzy integrator at the level of the sub modules, and with a gating network at the level of the modules.
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Manoatl Lopez, Edgar; Coello Coello, Carlos A.
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The success of local search techniques in the solution of combinatorial optimization problems has motivated their incorporation into multiobjective evolutionary algorithms, giving rise to the socalled multiobjective memetic algorithms (MOMAs). The main advantage for adopting this sort of hybridization is to speed up convergence to the Pareto front. However, the use of MOMAs introduces new issues, such as how to select the solutions to which the local search will be applied and for how long to run the local search engine (the use of such a local search engine has an extra computational cost). Here, we propose a new MOMA which switches between a hypervolumebased global optimizer and an IGD+based local search engine. Our proposed local search engine adopts a novel clustering technique based on the IGD+ indicator for splitting the objective space into subregions. Since both computing the hypervolume and applying a local search engine are very costly procedures, we propose a GPUbased parallelization of our algorithm. Our preliminary results indicate that our MOMA is able to converge faster than SMSEMOA to the true Pareto front of multiobjective problems having different degrees of difficulty.
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Sánchez, Daniela; Melin, Patricia; Castillo, Oscar
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In this paper we propose a new model of a Modular Neural Network (MNN) with fuzzy integration based on granular computing. The topology and parameters of the model are optimized with a Hierarchical Genetic Algorithm (HGA). The model was applied to the case of human recognition to illustrate its applicability. The proposed method is able to divide the data automatically into sub modules, to work with a percentage of images and select which images will be used for training. We considered, to test this method, the problem of human recognition based on ear, and we used a database with 77 persons (with 4 images each person for this task).
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GarcíaVázquez, Mireya S.; GareaLlano, Eduardo; ColoresVargas, Juan M.; ZamudioFuentes, Luis M.; RamírezAcosta, Alejandro A.
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Iris recognition is being widely used in different environments where the identity of a person is necessary. Therefore, it is a challenging problem to maintain high reliability and stability of this kind of systems in harsh environments. Iris segmentation is one of the most important process in iris recognition to preserve the abovementioned characteristics. Indeed, iris segmentation may compromise the performance of the entire system. This paper presents a comparative study of four segmentation algorithms in the frame of the high reliability iris verification system. These segmentation approaches are implemented, evaluated and compared based on their accuracy using three unconstraint databases, one of them is a video iris database. The result shows that, for an ultrahigh security system on verification at FAR = 0.01 %, segmentation 3 (Viterbi) presents the best results.
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Figueroa, Karina; Paredes, Rodrigo
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Proximity searching consists in retrieving objects out of a database similar to a given query. Nowadays, when multimedia databases are growing up, this is an elementary task. The permutation based index (PBI) and its variants are excellent techniques to solve proximity searching in high dimensional spaces, however they have been surmountable in low dimensional ones. Another PBI’s drawback is that the distance between permutations cannot allow to discard elements safely when solving similarity queries.
In the following, we introduce an improvement on the PBI that allows to produce a better promissory order using less space than the basic permutation technique and also gives us information to discard some elements. To do so, besides the permutations, we quantize distance information by defining distance rings around each permutant, and we also keep this data. The experimental evaluation shows we can dramatically improve upon specialized techniques in low dimensional spaces. For instance, in the real world dataset of NASA images, our boosted PBI uses up to 90 % less distances evaluations than AESA’s, the stateoftheart searching algorithm with the best performance in this particular space.
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LazoCortés, Manuel S.; MartínezTrinidad, José Fco.; CarrascoOchoa, J. A.
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This paper studies the relationship between the two most common definitions of reduct. Although there are other definitions, almost all the literature published in the framework of the Theory of Rough Sets, uses one of the two definitions we study here. However, there is an ambiguity in the use of these definitions and often authors do not previously declare what definition they refer to. Moreover, there are no publications where the relation between these two definitions is widely discussed, just that is what this paper addresses. We enunciate and demonstrate several properties expressing relations between both definitions including some illustrative examples.
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Tovar, Mireya; Pinto, David; Montes, Azucena; Serna, Gabriel; Vilariño, Darnes
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In this paper we present an approach for the automatic identification of relations in ontologies of restricted domain. We use the evidence found in a corpus associated to the same domain of the ontology for determining the validity of the ontological relations. Our approach employs formal concept analysis, a method used for the analysis of data, but in this case used for relations discovery in a corpus of restricted domain. The approach uses two variants for filling the incidence matrix that this method employs. The formal concepts are used for evaluating the ontological relations of two ontologies. The performance obtained was about 96 for taxonomic relations and 100 % for nontaxonomic relations, in the first ontology. In the second it was about 92 % for taxonomic relations and 98 % for nontaxonomic relations.
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Navarro, René F.; Rodríguez, Marcela D.; Favela, Jesús
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Even in its early stages, the cognitive deficits in persons with dementia (PwD) can produce significant functional impairment. Dementia is characterized by changes in personality and behavioral functioning that can be very challenging for caregivers and patients. This paper presents results on the use and adoption of a cognition assistive system to support occupational therapy to address psychological and behavioral symptoms of dementia. During 6 months we conducted an in situ system evaluation with a caregiverPwD dyad to evaluate the adoption and effectiveness of the system to ameliorate challenging behaviors. Evaluation results indicate that intervention personalization and touchbased systems interfaces encouraged the adoption and the positive effect in reducing challenging behaviors in PwD and decreases caregiver burden.
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Figueroa, Karina; Paredes, Rodrigo
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The permutation based index has shown to be very effective in medium and high dimensional metric spaces, even in difficult problems such as solving reverse knearest neighbor queries. Nevertheless, currently there is no study about which are the desirable features one can ask to a permutant set, or how to select good permutants. Similar to the case of pivots, our experimental results show that, compared with a randomly chosen set, a good permutant set yields to fast query response or to reduce the amount of space used by the index. In this paper, we start by characterizing permutants and studying their predictive power; then we propose an effective heuristic to select a good set of permutant candidates. We also show empirical evidence that supports our technique.
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Ita Luna, Guillermo; MarcialRomero, J. Raymundo
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We present some results about the parametric complexity of #2SAT and #2UNSAT, which consist on counting the number of models and falsifying assignments, respectively, for two Conjunctive Forms (2CF’s) . Firstly, we show some cases where given a formula F, #2SAT(F) can be bounded above by considering a binary pattern analysis over its set of clauses. Secondly, since #2SAT(F) = 2^{n}#2UNSAT(F) we show that, by considering the constrained graph G_{F} of F, if G_{F} represents an acyclic graph then, #UNSAT(F) can be computed in polynomial time. To the best of our knowledge, this is the first time where #2UNSAT is computed through its constrained graph, since the inclusionexclusion formula has been commonly used for computing #UNSAT(F).
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Goltsev, Alexander; Gritsenko, Vladimir; Kussul, Ernst; Baidyk, Tatiana
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We propose an algorithm for finding a set of texture features characterizing the most homogeneous texture area of an input image. The found set of features is intended for extraction of this segment. The algorithm processes any input images in the absence of any preliminary information about the images and, accordingly, without any learning. The essence of the algorithm is as follows. The image is covered with a number of test windows. In each of them, a degree of texture homogeneity is measured. The test window with maximal degree of homogeneity is determined and a representative patch of pixels is detected. The texture features extracted from the detected representative patch is considered as those that best characterize the most homogeneous texture segment. So, the proposed algorithm facilitates solution of the texture segmentation task by providing a segmentation technique with helpful additional information about the analyzed image. A computer program simulating the algorithm has been created. The program is tested on natural grayscale images.
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Rodríguez, Lisbeth; Li, Xiaoou; MejíaAlvarez, Pedro
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Vertical partitioning is a well known technique to improve query response time in relational databases. This consists in dividing a table into a set of fragments of attributes according to the queries run against the table. In dynamic systems the queries tend to change with time, so it is needed a dynamic vertical partitioning technique which adapts the fragments according to the changes in query patterns in order to avoid long query response time. In this paper, we propose an active system for dynamic vertical partitioning of relational databases, called DYVEP (DYnamic VErtical Partitioning). DYVEP uses active rules to vertically fragment and refragment a database without intervention of a database administrator (DBA), maintaining an acceptable query response time even when the query patterns in the database suffer changes. Experiments with the TPCH benchmark demonstrate efficient query response time.
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Batyrshin, Ildar; Sheremetov, Leonid
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The methods of pattern recognition in time series based on moving approximation (MAP) transform and MAP image of patterns are proposed. We discuss main properties of MAP transform, introduce a concept of a MAP image of time series and distance between time series patterns based on this concept which were used for recognition of small patterns in noisy time series. To illustrate the application of this technique to recognition of perception based patterns given by sequence of slopes, an example of recognition of water production patterns in petroleum wells used in expert system for diagnosis of water production problems is considered.
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OlveraLópez, J. Arturo; MartínezTrinidad, J. Francisco; CarrascoOchoa, J. Ariel
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In supervised classification, the object selection or instance selection is an important task, mainly for instancebased classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new mixed data object selection method based on clustering and border objects. We carried out an experimental comparison between our method and other object selection methods using some mixed data classifiers.
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Escalante, Hugo Jair; AcostaMendoza, Niusvel; MoralesReyes, Alicia; GagoAlonso, Andrés
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The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust weights of base classifiers or to select ensemble members. Usually, a weighted sum is used for combining classifiers outputs in both classical and evolutionary approaches. This study proposes a novel genetic program that learns a fusion function for combining heterogeneousclassifiers outputs. It evolves a population of fusion functions in order to maximize the classification accuracy. Highly nonlinear functions are obtained with the proposed method, subsuming the existing weightedsum formulations. Experimental results show the effectiveness of the proposed approach, which can be used not only with heterogeneous classifiers but also with homogeneousclassifiers and under bagging/boosting based formulations.
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GarcíaPerera, L. Paola; MexPerera, Carlos; NolazcoFlores, Juan A.
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In this research we present a new scheme for the generation of a biometric key based on Automatic Speech Technology and Support Vector Machines. Keys are produced by making a distinction among the voice attributes of the users employing hyperplanes. It is described how the key is conformed and the reliability of the method. We depict an experimental evaluation for different values of the parameters. Among the different kernels for the Support Vector Machine, the RBF obtained the best results.
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Vallejo, Edgar E.; Taylor, Charles E.
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This paper presents a computational framework for studying the influence of learning on the evolution of avian communication. We conducted computer simulations for exploring the effects of different learning strategies on the evolution of bird song. Experimental results show the genetic assimilation of song repertoires as a consequence of interactions between learning and evolution.
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TecuanhuehueVera, P.; CarrascoOchoa, Jesús Ariel; MartínezTrinidad, José Fco.
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Multidimensional scaling maps a set of ndimensional objects into a lowerdimension space, usually the Euclidean plane, preserving the distances among objects in the original space. Most algorithms for multidimensional scaling have been designed to work on numerical data, but in soft sciences, it is common that objects are described using quantitative and qualitative attributes, even with some missing values. For this reason, in this paper we propose a genetic algorithm especially designed for multidimensional scaling over mixed and incomplete data. Some experiments using datasets from the UCI repository, and a comparison against a common algorithm for multidimensional scaling, shows the behavior of our proposal.
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LoyolaGonzález, Octavio; MartínezTrinidad, José Fco.; CarrascoOchoa, Jesús Ariel; GarcíaBorroto, Milton
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Applying resampling methods is an important approach for working with class imbalance problems. The main reason is that many classifiers are sensitive to class distribution, biasing their prediction towards the majority class. Contrast pattern based classifiers are sensitive to imbalanced databases because these classifiers commonly find several patterns of the majority class and only a few patterns (or none) of the minority class. In this paper, we present a correlation study among resampling methods for contrast pattern based classifiers. Our experiments performed over several imbalanced databases show that there is a high correlation among different resampling methods. Correlation results show that there are nine different groups with very high inner correlation and very low outer correlation. We show that most resampling methods allow improving the accuracy of the contrast pattern based classifiers.
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Chacon, Mario I.; Alonso, Graciela Ramirez
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This paper describes the design and implementation of a wood defect classifier. The defects are four different types of knots found in wood surfaces. Classification is based on features obtained from Gabor filters and supervised and non supervised artificial neural networks are used as classifiers. A Selforganizing neural network and a fuzzy Selforganizing neural network were designed as classifiers. The fuzzy SONN shows a reduction on the training time and had a better performance. A final classifier, a feedforward perceptron using the weights of the fuzzy SONN as initial weights turn to be the best classifier with a performance of 97.22% in training and 91.17% in testing. The perceptron classifier surpasses a human inspector task which has a maximum performance of 85%.
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Tovar, Mireya; Pinto, David; Montes, Azucena; Vilariño, Darnes
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Measuring the degree of semantic similarity for word pairs is very challenging task that has been addressed by the computational linguistics community in the recent years. In this paper, we propose a method for evaluating input word pairs in order to measure the degree of semantic similarity. This unsupervised method uses a prototype vector calculated on the basis of word pair representative vectors which are contructed by using snippets automatically gathered from the world wide web.
The obtained results shown that the approach based on prototype vectors outperforms the results reported in the literature for a particular semantic similarity class.
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Levachkine, Serguei; Velàzquez, Aurelio; Alexandrov, Victor; Kharinov, Mikhail
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Semantic analysis of cartographic images is interpreted as a separate representation of cartographic patterns (alphanumeric, punctual, linear, and area). We present an approach to map interpretation exploring the idea of synthesis of invariant graphic images at low level processing (vectorization and segmentation). This means that we ran “vectorizationrecognition” and “segmentationinterpretation” systems simultaneously. Although these systems can generate some errors in interpretation, they are much more useful for the following understanding algorithms because its output is nearly recognized objects of interest.
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EscobarAcevedo, Adelina; MontesyGómez, Manuel; VillaseñorPineda, Luis
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Crosslanguage text classification (CLTC) aims to take advantage of existing training data from one language to construct a classifier for another language. In addition to the expected translation issues, CLTC is also complicated by the cultural distance between both languages, which causes that documents belonging to the same category concern very different topics. This paper proposes a reclassification method which purpose is to reduce the errors caused by this phenomenon by considering information from the own target language documents. Experimental results in a news corpus considering three pairs of languages and four categories demonstrated the appropriateness of the proposed method, which could improve the initial classification accuracy by up to 11%.
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RiveraRovelo, Jorge; Herold, Silena; BayroCorrochano, Eduardo
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In this paper we present a method based on selforganizing neural networks to extract the shape of a 2D or 3D object using a set of transformations expressed as versors in the conformal geometric algebra framework. Such transformations, when applied to any geometric entity of this geometric algebra, define the shape of the object. This approach was tested with several images, but here we show its utility first using a 2D magnetic resonance image to segment the ventricle. Then we present some examples of an application for the case of 3D objects.
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Lopez, Alejandro; Cienfuegos, Miguel; Ainseba, Bedreddine; Bendahmane, Mostafa
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In this paper we present a nearest neighbor particle swarm optimization (PSO) algorithm applied to the numerical analysis of the inverse problem in electrocardiography. A twostep algorithm is proposed based on the application of the modified PSO algorithm with the Tikhonov regularization method to calculate the potential distribution in the heart. The PSO improvements include the use of the neighborhood particles as a strategy to balance exploration and exploitation in order to prevent premature convergences and produce a better local search. In the literature the inverse problem in electrocardiography is solved using the minimum energy norm in a Tikhonov regularization scheme. Although this approach solves the system, the solution may not have a meaning in the physical sense. Comparing to the classical reconstruction, the twostep PSO algorithm improves the accuracy of the solution with respect to the original distribution. Finally, to validate our results, we create a distribution over the heart by using a model of electrical activity (Bidomain model) coupled with a volume conductor model for the torso. Then, using our method, we make the reconstruction of the potential distribution.
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BayroCorrochano, Eduardo
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In this paper the authors use the framework of geometric algebra for applications in computer vision, robotics and learning . This mathematical system keeps our intuitions and insight of the geometry of the problem at hand and it helps us to reduce considerably the computational burden of the problems. The authors show that framework of geometric algebra can be in general of great advantage for applications using stereo vision, range data, laser, omnidirectional and odometry based systems. For learning the paper presents the Clifford Support Vector Machines as a generalization of the real and complexvalued Support Vector Machines.
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MedinaPérez, Miguel Angel; GarcíaBorroto, Milton; GutierrezRodríguez, Andres Eduardo; AltamiranoRobles, Leopoldo
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Developing accurate fingerprint verification algorithms is an active research area. A large amount of fingerprint verification algorithms are based on minutiae descriptors. An important component of these algorithms is the alignment strategy. The single alignment strategy, with O(n^{2}) time complexity, uses the local matching minutiae pair that maximizes the similarity value to align the minutiae. Nevertheless, even if the selected minutiae pair is a true matching pair, it is not necessarily the best pair to carry out fingerprint alignment. The multiple alignments strategy alleviates these limitations by performing multiple minutiae alignments, increasing the time complexity to O(n^{4}). In this paper, we improve the multiple alignment strategy, reducing its complexity while still achieving a high accuracy. The new strategy is based on the rationale that most minutiae descriptors from one fingerprint correspond with their most similar descriptors from the other fingerprint. To test the new strategy behavior, we adapt three well known algorithms to a traditional multiple alignment strategy and to our strategy. Several experiments in the FVC2004 database show that our strategy outperforms both the single and the multiple alignments strategies.
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LemuzLópez, Rafael; AriasEstrada, Miguel
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In this paper we address the problem of recovering the threedimensional shape of an object and the motion of the camera based on multiple feature correspondences from an image sequence. We present a new incremental projective factorization algorithm using a perspective camera model. The original projective factorization method produces robust results. However, the method can not be applied to realtime applications since it is based on a batch processing pipeline and the size of the data matrix grows with each additional frame. The proposed algorithm obtains an estimate of shape and motion for each additional frame adding a dimension reduction step. A subset of frames is selected analyzing the contribution of frames to the reconstruction quality. The main advantage of the novel algorithm is the reduction of the computational cost while keeping the robustness of the original method. Experiments with synthetic and real images illustrate the accuracy and performance of the new algorithm.
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Domínguez, A. Rojas; LaraAlvarez, Carlos; Bayro, Eduardo
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A novel method for automated identification of banknotes’ denominations based on image processing is presented. The method is part of a wearable aiding system for the visually impaired, and it uses a standard video camera as the image collecting device. The method first extracts points of interest from the denomination region of a banknote and then performs an analysis of the geometrical patterns so defined, which allows the identification of the banknote denomination. Experiments were performed with a testsubject in order to simulate realworld operating conditions. A high average identification rate was achieved.
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Montero, José Antonio; Sucar, L. Enrique
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Most gesture recognition systems are based only on hand motion information, and are designed mainly for communicative gestures. However, many activities of everyday life involve interaction with surrounding objects. We propose a new approach for the recognition of manipulative gestures that interact with objects in the environment. The method uses nonintrusive visionbased techniques. The hands of a person are detected and tracked using an adaptive skin color segmentation process, so the system can operate in a wide range of lighting conditions. Gesture recognition is based on hidden Markov models, combining motion and contextual information, where the context refers to the relation of the position of the hand with other objects. The approach was implemented and evaluated on two different domains: video conference and assistance, obtaining gesture recognition rates from 94 % to 99.47 %. The system is very efficient so it is adequate for use in realtime applications.
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SanchezDiaz, Guillermo; PizaDavila, Ivan; LazoCortes, Manuel; MoraGonzalez, Miguel; SalinasLuna, Javier
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Typical testors are a useful tool for both feature selection and for determining feature relevance in supervised classification problems. Nowadays, generating all typical testors of a training matrix is computationally expensive; all reported algorithms have exponential complexity, depending mainly on the number of columns in the training matrix. For this reason, different approaches such as sequential and parallel algorithms, genetic algorithms and hardware implementations techniques have been developed. In this paper, we introduce a fast implementation of the algorithm CT_EXT (which is one of the fastest algorithms reported) based on an accumulative binary tuple, developed for generating all typical testors of a training matrix. The accumulative binary tuple implemented in the CT_EXT algorithm, is a useful way to simplifies the search of feature combinations which fulfill the testor property, because its implementation decreases the number of operations involved in the process of generating all typical testors. In addition, experimental results using the proposed fast implementation of the CT_EXT algorithm and the comparison with other state of the art algorithms that generated typical testors are presented.
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Lara–Alvarez, Carlos; Rojas, Alfonso; Bayro–Corrochano, Eduardo
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The Place Recognition (PR) problem is fundamental for real time applications such as mobile robots (e.g. to detect loop closures) and guidance systems for the visually impaired. The Bag of Words (BoW) is a conventional approach that calculates a histogram of frequencies. One of the disadvantages of the BoW representation is that it loses information about the spatial location of features in the image. In this paper we study an approximate index based on the classic q–gram paradigm to recover images. Similar to the BoW, our approach detects interest points and assigns labels. Each image is represented by a set of q–grams obtained from triangles of a Delaunay decomposition. This representation allows us to create an index and to recover images efficiently. The proposed approach is path independent and was tested with a publicly available dataset showing a high recall rate and reduced time complexity.
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SilvánCárdenas, José Luis; Wang, Le
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Building footprint geometry is a basic layer of information required by government institutions for a number of land management operations and research. LiDAR (light detection and ranging) is a laserbased altimetry measurement instrument that is flown over relatively wide land areas in order to produce digital surface models. Although high spatial resolution LiDAR measurements (of around 1 m horizontally) are suitable to detect aboveground features through elevation discrimination, the automatic extraction of buildings in many cases, such as in residential areas with complex terrain forms, has proved a difficult task. In this study, we developed a method for detecting building footprint from LiDAR altimetry data and tested its performance over four sites located in Austin, TX. Compared to another standard method, the proposed method had comparable accuracy and better efficiency.
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GarciaCeja, Enrique; Brena, Ramon; GalvánTejada, Carlos E.
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Most of the previous works in hand gesture recognition focus in increasing the accuracy and robustness of the systems, however little has been done to understand the context in which the gestures are performed, i.e, the same gesture could mean different things depending on the context and situation. Understanding the context may help to build more userfriendly and interactive systems. In this work, we used location information in order to contextualize the gestures. The system constantly identifies the location of the user so when he/she performs a gesture the system can perform an action based on this information.
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Zapotecas Martínez, Saúl; Sosa Hernández, Víctor A.; Aguirre, Hernán; Tanaka, Kiyoshi; Coello Coello, Carlos A.
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The design of selection mechanisms based on quality assessment indicators has become one of the main research topics in the development of MultiObjective Evolutionary Algorithms (MOEAs). Currently, most indicatorbased MOEAs have employed the hypervolume indicator as their selection mechanism in the search process. However, hypervolumebased MOEAs become inefficient (and eventually, unaffordable) as the number of objectives increases. In this paper, we study the construction of a reference set from a family of curves. Such reference set is used together with a performance indicator (namely Δ_{p}) to assess the quality of solutions in the evolutionary process of an MOEA. We show that our proposed approach is able to deal (in an efficient way) with problems having many objectives (up to ten objective functions). Our preliminary results indicate that our proposed approach is highly competitive with respect to two stateoftheart MOEAs over the set of test problems that were adopted in our comparative study.
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Alejo, R.; Sotoca, J. M.; Valdovinos, R. M.; Toribio, P.
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The quality and size of the training data sets is a critical stage on the ability of the artificial neural networks to generalize the characteristics of the training examples. Several approaches are focused to form training data sets by identification of border examples or core examples with the aim to improve the accuracy of network classification and generalization. However, a refinement of data sets by the elimination of outliers examples may increase the accuracy too. In this paper, we analyze the use of different editing schemes based on nearest neighbor rule on the most popular neural networks architectures.
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Figueroa, Karina; Paredes, Rodrigo; CamarenaIbarrola, J. Antonio; Reyes, Nora
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Similarity searching consists in retrieving from a database the objects, also known as nearest neighbors, that are most similar to a given query, it is a crucial task to several applications of the pattern recognition problem. In this paper we propose a new technique to reduce the number of comparisons needed to locate the nearest neighbors of a query. This new index takes advantage of two known algorithms: FHQT (Fixed Height Queries Tree) and PBA (PermutationBased Algorithm), one for low dimension and the second for high dimension. Our results show that this combination brings out the best of both algorithms, this winner combination of FHQT and PBA locates nearest neighbors up to four times faster in high dimensions leaving the known well performance of FHQT in low dimensions unaffected.
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Pinto, David; Vilariño, Darnes; Balderas, Carlos; Tovar, Mireya; Beltrán, Beatriz
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Word Sense Disambiguation (WSD) is considered one of the most important problems in Natural Language Processing [1]. It is claimed that WSD is essential for those applications that require of language comprehension modules such as search engines, machine translation systems, automatic answer machines, second life agents, etc. Moreover, with the huge amounts of information in Internet and the fact that this information is continuosly growing in different languages, we are encourage to deal with crosslingual scenarios where WSD systems are also needed. On the other hand, Lexical Substitution (LS) refers to the process of finding a substitute word for a source word in a given sentence. The LS task needs to be approached by firstly disambiguating the source word, therefore, these two tasks (WSD and LS) are somehow related. In this paper, we present a naïve approach to tackle the problem of crosslingual WSD and crosslingual lexical substitution. We use a bilingual statistical dictionary, which is calculated with Giza++ by using the EUROPARL parallel corpus, in order to calculate the probability of a source word to be translated to a target word (which is assumed to be the correct sense of the source word but in a different language). Two versions of the probabilistic model are tested: unweighted and weighted. The results were compared with those of an international competition, obtaining a good performance.
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Sandoval, Alejandro Cruz; Yu, Wen
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Dynamic neural networks with different timescales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed network be stable. In this paper, the passivitybased approach is used to derive stability conditions for dynamic neural networks with different timescales. Several stability properties, such as passivity, asymptotic stability, inputtostate stability and bounded input bounded output stability, are guaranteed in certain senses. Numerical examples are also given to demonstrate the effectiveness of the theoretical results.
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SalgadoGarza, Luis R.; NolazcoFlores, Juan A.
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This document describes the realization of a spoken information retrieval system and its application to words search in an indexed video database. The system uses an automatic speech recognition (ASR) software to convert the audio signal of a video file into a transcript file and then a document indexing tool to index this transcripted file. Then, a spoken query, uttered by any user, is presented to the ASR to decode the audio signal and propose a hypothesis that is later used to formulate a query to the indexed database. The final outcome of the system is a list of video frame tags containing the audio correspondent to the spoken query. The speech recognition system achieved less than 15% Word Error Rate (WER) and its combined operation with the document indexing system showed outstanding performance with spoken queries.
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RosalesPérez, Alejandro; Gonzalez, Jesus A.; CoelloCoello, Carlos A.; ReyesGarcia, Carlos A.; Escalante, Hugo Jair
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This paper introduces EMOPG+FS, a novel approach to prototype generation and feature selection that explicitly minimizes the classification error rate, the number of prototypes, and the number of features. Under EMOPG+FS, prototypes are initialized from a subset of training instances, whose positions are adjusted through a multiobjective evolutionary algorithm. The optimization process aims to find a set of suitable solutions that represent the best possible tradeoffs among the considered criteria. Besides this, we also propose a strategy for selecting a single solution from the several that are generated during the multiobjective optimization process.We assess the performance of our proposed EMOPG+FS using a suite of benchmark data sets and we compare its results with respect to those obtained by other evolutionary and nonevolutionary techniques. Our experimental results indicate that our proposed approach is able to achieve highly competitive results.
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HernándezRodríguez, Selene; MartínezTrinidad, J. Francisco; CarrascoOchoa, J. Ariel
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In this work, a fast k most similar neighbor (kMSN) classifier for mixed data is presented. The k nearest neighbor (kNN) classifier has been a widely used nonparametric technique in Pattern Recognition. Many fast kNN classifiers have been developed to be applied on numerical object descriptions, most of them based on metric properties to avoid object comparisons. However, in some sciences as Medicine, Geology, Sociology, etc., objects are usually described by numerical and non numerical features (mixed data). In this case, we can not assume the comparison function satisfies metric properties. Therefore, our classifier is based on search algorithms suitable for mixed data and nonmetric comparison functions. Some experiments and a comparison against other two fast kNN methods, using standard databases, are presented.
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Quintero, Rolando; Levachkine, Serguei; Torres, Miguel; Moreno, Marco
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In this work, we present an algorithm for increasing spectral resolution in DEM. The algorithm is based on the 8connected skeleton of polygons formed by the contour lines and to prune this skeleton by translating it into a graph. This is an alternative to the processes of vector interpolation. With this approach, it is possible to find new elevation data from the information contained in DEM and generate new data with the same spatial resolution.
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Barandela, Ricardo; Valdovinos, Rosa M.; Sánchez, J. Salvador; Ferri, Francesc J.
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The problem of imbalanced training sets in supervised pattern recognition methods is receiving growing attention. Imbalanced training sample means that one class is represented by a large number of examples while the other is represented by only a few. It has been observed that this situation, which arises in several practical domains, may produce an important deterioration of the classification accuracy, in particular with patterns belonging to the less represented classes. In this paper we present a study concerning the relative merits of several resizing techniques for handling the imbalance issue. We assess also the convenience of combining some of these techniques.
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Escarcega, David; Ramos, Fernando; Espinosa, Ana; Berumen, Jaime
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Cervical Cancer (CC) is the result of the infection of high risk Human Papilloma Viruses. mRNA microarray expression data provides biologists with evidences of cellular compensatory gene expression mechanisms in the CC progression. Pattern recognition of signalling pathways through expression data can reveal interesting insights for the understanding of CC. Consequently, gene expression data should be submitted to different preprocessing tasks. In this paper we propose a methodology based on the integration of expression data and signalling pathways as a needed phase for the pattern recognition within signaling CC pathways. Our results provide a topdown interpretation approach where biologists interact with the recognized patterns inside signalling pathways.
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