Moisés Fernández

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Within probabilistic classification problems, learning the Markov boundary of the class variable consists in the optimal approach for feature subset selection. In this paper we propose two algorithms that learn the Markov boundary of a selected variable. These algorithms are based on the score+search paradigm for learning Bayesian networks. Both algorithms(More)
We consider the scenario in which an automatic classifier (previously built) is available. It is used to classify new instances but, in some cases, the classifier may request the intervention of a human (the oracle), who gives it the correct class. In this scenario, first it is necessary to study how the performance of the system should be evaluated, as it(More)
—Feature subset selection is increasingly becoming an important preprocessing step within the field of automatic classification. This is due to the fact that the domain problems currently considered contain a high number of variables, and some kind of dimensionality reduction becomes necessary, in order to make the classification task approachable. In this(More)
Resumen En este artículo proponemos un algoritmo eficaz y eficiente, C-MB, capaz de aprender las fronteras de markov de una red bayesiana mediante el paradigma métrica+búsqueda. Sus resultados superan am-pliamente a los algoritmos PCMB, IAMB y KIAMB basados en tests de independencia (TIs) que actualmente forman parte del estado del arte y demuestran ser(More)
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