Abstract
AbstractThis work both describes and evaluates a Bayesian feature selection approach for classification problems. Basically, a Bayesian network is generated from a dataset, and then the Markov Blanket of the class variable is used to the feature subset selection task. The proposed methodology is illustrated by means of simulations in three datasets that are benchmarks for data mining methods: Wisconsin Breast Cancer, Mushroom and Congressional Voting Records. Three classifiers were employed to show the efficacy of the proposed method. The average classification rates obtained in the datasets formed by all features are compared to those achieved in the datasets formed by the features that belong to the Markov Blanket. The performed simulations lead to interesting results.
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