Wrappers for Feature Subset Selection

In the feature subset selection problem a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention while ignoring the rest To achieve the best possible performance with a particular learning algorithm on a particular training set a feature subset selection method should consider how the algorithm and the training set interact We explore the relation between optimal feature subset selection and relevance Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain We study the strengths and weaknesses of the wrapper approach and show a series of improved designs We compare the wrapper approach to induction without feature subset selection and to Relief a lter approach to feature subset selection Signi cant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used decision trees and Naive Bayes