Irrelevant Features and the Subset Selection Problem
نویسندگان
چکیده
We address the problem of nding a subset of features that allows a supervised induc tion algorithm to induce small high accuracy concepts We examine notions of relevance and irrelevance and show that the de nitions used in the machine learning literature do not adequately partition the features into useful categories of relevance We present de ni tions for irrelevance and for two degrees of relevance These de nitions improve our un derstanding of the behavior of previous sub set selection algorithms and help de ne the subset of features that should be sought The features selected should depend not only on the features and the target concept but also on the induction algorithm We describe a method for feature subset selection using cross validation that is applicable to any in duction algorithm and discuss experiments conducted with ID and C on arti cial and real datasets
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