Minimum redundancy maximum relevancy versus score-based methods for learning Markov boundaries
Silvia Acid, Luis M. de Campos, Moisés Fernández · 2011
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 paper we make an experimental comparison between a state-of-the-art method for feature selection, namely minimum Redundancy Maximum Relevance, and a recently proposed method for learning Markov boundaries based on searching for Bayesian network structures in constrained spaces using standard scoring functions.