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83 results on '"Carrasco-Ochoa, J. Ariel"'

5. Ranking Based Unsupervised Feature Selection Methods: An Empirical Comparative Study in High Dimensional Datasets

Author

Solorio-Fernández, Saúl, Carrasco-Ochoa, J. Ariel, Martínez-Trinidad, José Fco., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Batyrshin, Ildar, editor, Martínez-Villaseñor, María de Lourdes, editor, and Ponce Espinosa, Hiram Eredín, editor

Published
2018
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6. Fast-BR vs. Fast-CT_EXT: An Empirical Performance Study

Author

Rodríguez-Diez, Vladímir, Martínez-Trinidad, José Fco., Carrasco-Ochoa, J. Ariel, Lazo-Cortés, Manuel S., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Carrasco-Ochoa, Jesús Ariel, editor, Martínez-Trinidad, José Francisco, editor, and Olvera-López, José Arturo, editor

Published
2017
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7. An Algorithm for Computing Goldman Fuzzy Reducts

Author

Carrasco-Ochoa, J. Ariel, Lazo-Cortés, Manuel S., Martínez-Trinidad, José Fco., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Carrasco-Ochoa, Jesús Ariel, editor, Martínez-Trinidad, José Francisco, editor, and Olvera-López, José Arturo, editor

Published
2017
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10. Hybrid Feature Selection Method for Supervised Classification Based on Laplacian Score Ranking

Author

Solorio-Fernández, Saúl, Carrasco-Ochoa, J. Ariel, Martínez-Trinidad, José Fco., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Martínez-Trinidad, José Francisco, editor, and Carrasco-Ochoa, Jesús Ariel, editor

Published
2010
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12. A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets

Author

Franco-Arcega, Anilu, Carrasco-Ochoa, J. Ariel, Sánchez-Díaz, Guillermo, Martínez-Trinidad, J. Fco, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fyfe, Colin, editor, Kim, Dongsup, editor, Lee, Soo-Young, editor, and Yin, Hujun, editor

Published
2008
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13. Prototype Selection Via Prototype Relevance

Author

Olvera-López, J. Arturo, Carrasco-Ochoa, J. Ariel, Martínez-Trinidad, J. Fco., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Ruiz-Shulcloper, José, editor, and Kropatsch, Walter G., editor

Published
2008
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16. FPGA-Based Architecture for Computing Testors

Author

Rojas, Alejandro, Cumplido, René, Carrasco-Ochoa, J. Ariel, Feregrino, Claudia, Martínez-Trinidad, J. Francisco, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yin, Hujun, editor, Tino, Peter, editor, Corchado, Emilio, editor, Byrne, Will, editor, and Yao, Xin, editor

Published
2007
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17. Fast k Most Similar Neighbor Classifier for Mixed Data Based on a Tree Structure

Author

Hernández-Rodríguez, Selene, Martínez-Trinidad, J. Francisco, Carrasco-Ochoa, J. Ariel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Rueda, Luis, editor, and Mery, Domingo, editor

Published
2007
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18. Mixed Data Object Selection Based on Clustering and Border Objects

Author

Olvera-López, J. Arturo, Martínez-Trinidad, J. Francisco, Carrasco-Ochoa, J. Ariel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Rueda, Luis, editor, and Mery, Domingo, editor

Published
2007
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19. On the Design and Implementation of a High Performance Configurable Architecture for Testor Identification

Author

Cumplido, René, Carrasco-Ochoa, J. Ariel, Feregrino, Claudia, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Martínez-Trinidad, José Francisco, editor, and Carrasco Ochoa, Jesús Ariel, editor

Published
2006
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20. Conceptual K-Means Algorithm Based on Complex Features

Author

Ayaquica-Martínez, I. O., Martínez-Trinidad, J. Fco., Carrasco-Ochoa, J. Ariel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Martínez-Trinidad, José Francisco, editor, and Carrasco Ochoa, Jesús Ariel, editor

Published
2006
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21. Edition Schemes Based on BSE

Author

Olvera-López, J. Arturo, Martínez-Trinidad, J. Fco., Carrasco-Ochoa, J. Ariel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sanfeliu, Alberto, editor, and Cortés, Manuel Lazo, editor

Published
2005
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22. Sequential Search for Decremental Edition

Author

Olvera-López, José A., Carrasco-Ochoa, J. Ariel, Martínez-Trinidad, José Fco., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gallagher, Marcus, editor, Hogan, James P., editor, and Maire, Frederic, editor

Published
2005
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36. Extensions to AGraP Algorithm for Finding a Reduced Set of Inexact Graph Patterns.

Author

Flores-Garrido, Marisol, Carrasco-Ochoa, J. Ariel, and Martínez-Trinidad, José Fco.

Subjects
*PATTERN recognition systems, *DATA mining, *ISOMORPHISM (Mathematics), *CLASSIFICATION algorithms, *GRAPH algorithms
Abstract

Most algorithms to mine graph patterns, during the searching process, require a pattern to be identical to its occurrences, relying on the graph isomorphism problem. However, in recent years, there has been interest in the case in which it is acceptable to have some differences between a pattern and its occurrences, whether these differences are in labels or in structure. Allowing some differences and using inexact matching to measure the similarity between graphs lead to the discovery of new patterns, but some important challenges, such as the increment on the number of found patterns, make the post-mining analysis harder. In this work we focus on two extensions of the AGraP algorithm, which mines inexact patterns, addressing the issue of reducing the output pattern set while trying to retain the useful information gained through the use of inexact matching. First, exploring a traditional approach, we propose the CloseAFG algorithm that focuses on closed patterns. Then, we propose the IntAFG algorithm to find a subset of patterns covering the original pattern set, while lessening redundancy among selected patterns. We show the performance of our approaches through some experiments on synthetic databases; additionally, we also show the usefulness of the reduced pattern sets for image classification. [ABSTRACT FROM AUTHOR]

Published
2018
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44. Mixed Data Object Selection Based on Clustering and Border Objects.

Author

Hutchison, David, Kanade, Takeo, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Rueda, Luis, Mery, Domingo, Kittler, Josef, Olvera-López, J. Arturo, Martínez-Trinidad, J. Francisco, and Carrasco-Ochoa, J. Ariel

Abstract

In supervised classification, the object selection or instance selection is an important task, mainly for instance-based 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. [ABSTRACT FROM AUTHOR]

Published
2008
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45. Fast k Most Similar Neighbor Classifier for Mixed Data Based on a Tree Structure.

Author

Hutchison, David, Kanade, Takeo, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Rueda, Luis, Mery, Domingo, Kittler, Josef, Hernández-Rodríguez, Selene, Martínez-Trinidad, J. Francisco, and Carrasco-Ochoa, J. Ariel

Abstract

In this work, a fast k most similar neighbor (k-MSN) classifier for mixed data is presented. The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition. Many fast k-NN 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 non-metric comparison functions. Some experiments and a comparison against other two fast k-NN methods, using standard databases, are presented. [ABSTRACT FROM AUTHOR]

Published
2008
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46. Object Selection Based on Clustering and Border Objects.

Author

Kacprzyk, J., Kurzynski, Marek, Puchala, Edward, Wozniak, Michal, Zolnierek, Andrzej, Olvera-López, J. Arturo, Carrasco-Ochoa, J. Ariel, and Martínez-Trinidad, J. Francisco

Abstract

Object selection is an important task for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new method based on clustering which tries to find border objects that contribute with useful information allowing to the classifier discriminating between classes. An experimental comparison of our method, the CLU method based on clustering, and the DROP methods, is presented. [ABSTRACT FROM AUTHOR]

Published
2008
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47. Fast Most Similar Neighbor Classifier for Mixed Data.

Author

Carbonell, Jaime G., Siekmann, Jörg, Kobti, Ziad, Wu, Dan, Hernández-Rodríguez, Selene, Martínez-Trinidad, J. Francisco, and Carrasco-Ochoa, J. Ariel

Abstract

The nearest neighbor (NN) classifier has been a widely used technique in pattern recognition because of its simplicity and good behavior. To decide the class of a new object, the NN classifier performs an exhaustive comparison between the object to classify and the training set T. However, when T is large, the exhaustive comparison is very expensive and sometimes becomes inapplicable. To avoid this problem, many fast NN algorithms have been developed for numerical object descriptions, most of them based on metric properties to avoid comparisons. However, in some sciences as Medicine, Geology, Sociology, etc., objects are usually described by numerical and non numerical attributes (mixed data). In this case, we can not assume the comparison function satisfies metric properties. Therefore, in this paper a fast most similar object classifier based on search methods suitable for mixed data is presented. Some experiments using standard databases and a comparison with other two fast NN methods are presented. [ABSTRACT FROM AUTHOR]

Published
2007
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48. On the Design and Implementation of a High Performance Configurable Architecture for Testor Identification.

Author

Martínez-Trinidad, José Francisco, Carrasco Ochoa, Jesús Ariel, Kittler, Josef, Cumplido, René, Carrasco-Ochoa, J. Ariel, and Feregrino, Claudia

Abstract

Typical testors are a useful tool to do feature selection in supervised classification problems with mixed incomplete data. However, the complexity of computing all typical testors of a training matrix has an exponential growth with respect to the number of columns in the matrix. For this reason different approaches like heuristic algorithms, parallel and distributed processing, have been developed. In this paper, we present a configurable custom architecture for the efficient identification of testors from a given input matrix. The architectural design is based on a brute force approach that is suitable for high populated input matrixes. The architecture has been designed to deal with parallel processing and can be configured for any size of matrix. The architecture is able to evaluate if a vector is a testor of the matrix in a single clock cycle. The architecture has been implemented on a Field Programmable Gate Array (FPGA) device. Results show that it provides runtime improvements over software implementations running on state-of-the-art processors. FPGA implementation results are presented and implications to the field of pattern recognition discussed. [ABSTRACT FROM AUTHOR]

Published
2006
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49. Conceptual K-Means Algorithm Based on Complex Features.

Author

Martínez-Trinidad, José Francisco, Carrasco Ochoa, Jesús Ariel, Kittler, Josef, Ayaquica-Martínez, I. O., Martínez-Trinidad, J. Fco., and Carrasco-Ochoa, J. Ariel

Abstract

The k-means algorithm is the most studied and used tool for solving the clustering problem when the number of clusters is known a priori. Nowadays, there is only one conceptual version of this algorithm, the conceptual k-means algorithm. One characteristic of this algorithm is the use of generalization lattices, which define relationships among the feature values. However, for many applications, it is difficult to determine the best generalization lattices; moreover there are not automatic methods to build the lattices, thus this task must be done by the specialist of the area in which we want to solve the problem. In addition, this algorithm does not work with missing data. For these reasons, in this paper, a new conceptual k-means algorithm that does not use generalization lattices to build the concepts and allows working with missing data is proposed. We use complex features for generating the concepts. The complex features are subsets of features with associated values that characterize objects of a cluster and at the same time not characterize objects from other clusters. Some experimental results obtained by our algorithm are shown and they are compared against the results obtained by the conceptual k-means algorithm. [ABSTRACT FROM AUTHOR]

Published
2006
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50. Edition Schemes Based on BSE.

Author

Sanfeliu, Alberto, Cortés, Manuel Lazo, Olvera-López, J. Arturo, Martínez-Trinidad, J. Fco., and Carrasco-Ochoa, J. Ariel

Abstract

Edition is an important and useful task in supervised classification specifically for instance-based classifiers because edition discards from the training set those useless or harmful objects for the classification accuracy and it helps to reduce the size of the original training sample and to increase both the classification speed and accuracy. In this paper, we propose two edition schemes that combine edition methods and sequential search for instance selection. In addition, we present an empirical comparison between these schemes and some other edition methods. [ABSTRACT FROM AUTHOR]

Published
2005
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