Zobrazeno 1 - 10
of 33
pro vyhledávání: '"J. Fco. Martínez-Trinidad"'
Autor:
Sebastián Bejos, Ivan Feliciano-Avelino, J. Fco. Martínez-Trinidad, José Ariel Carrasco-Ochoa
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 39:2137-2145
Document clustering has become an important task for processing the big amount of textual information available on the Internet. On the other hand, k-means is the most widely used algorithm for clustering, mainly due to its simplicity and effectivene
Autor:
Jesús Ariel Carrasco-Ochoa, Salvador Godoy-Calderon, Manuel S. Lazo-Cortés, Eduardo Alba-Cabrera, J. Fco. Martínez-Trinidad
Publikováno v:
IEEE Access, Vol 7, Pp 82809-82816 (2019)
This paper aims at studying the relationship between two rather relevant theoretic fields such as Graph Theory and Testor Theory, deepening in the unexploited relation between the concepts of Minimal Transversal and Irreducible Testor. First, the cla
Autor:
Manuel S. Lazo-Cortés, Guillermo Sanchez Diaz, J. Fco. Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa
Publikováno v:
Intelligent Data Analysis. 20:317-337
Attribute reduction is a very important task in Rough Set Theory. In this context, in recent years several attribute reduction algorithms based on the discernibility matrix have been proposed. In this paper, we go back to the binary discernibility ma
Autor:
J. Fco. Martínez-Trinidad, Milton García-Borroto, Andres Eduardo Gutierrez-Rodríguez, Jesús Ariel Carrasco-Ochoa
Publikováno v:
Intelligent Data Analysis. 19:1297-1310
In clustering, providing an explanation of the results is an important task. Pattern-based clustering algorithms return a set of patterns that describe the objects grouped in each cluster. The most recent algorithms proposed in this approach have a h
Autor:
Jesús Ariel Carrasco-Ochoa, J. Fco. Martínez-Trinidad, Andres Eduardo Gutierrez-Rodríguez, Milton García-Borroto
Publikováno v:
Knowledge-Based Systems. 82:70-79
Pattern-based clustering algorithms return a set of patterns that describe the objects of each cluster. The most recent algorithms proposed in this approach extract patterns on numerical datasets by applying an a priori discretization process, which
Autor:
J. Ariel Carrasco-Ochoa, Pablo Hernandez-Leal, J. Arturo Olvera-López, J. Fco. Martínez-Trinidad
Publikováno v:
Pattern Recognition. 46:365-375
Instance selection algorithms are used for reducing the number of training instances. However, most of them suffer from long runtimes which results in the incapability to be used with large datasets. In this work, we introduce an Instance Ranking per
Autor:
Anilu Franco-Arcega, Jesús Ariel Carrasco-Ochoa, Guillermo Sánchez-Díaz, J. Fco. Martínez-Trinidad
Publikováno v:
Intelligent Data Analysis. 16:649-664
Decision trees are commonly used in supervised classification. Currently, supervised classification problems with large training sets are very common, however many supervised classifiers cannot handle this amount of data. There are some decision tree
Publikováno v:
Expert Systems with Applications. 38:10018-10024
Commonly, when a feature selection algorithm is applied, a single feature subset is selected for all the classes, but this subset could be inadequate for some classes. Class-specific feature selection allows selecting a possible different feature sub
Publikováno v:
Pattern Recognition. 43:873-886
The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition, because of its simplicity and good performance. In order to decide the class of a new prototype, the k-NN classifier performs an exhaustiv
Autor:
Jesús Ariel Carrasco-Ochoa, J. Fco. Martínez-Trinidad, José Arturo Olvera-López, Josef Kittler
Publikováno v:
Intelligent Data Analysis. 13:599-631
In this paper, we propose and explore the use of the sequential search for solving the prototype selection problem since this kind of search has shown good performance for solving selection problems. We propose three prototype selection methods based