Wrappers for Feature Subset Selection

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Wrappers for Feature Subset Selection (1997)

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by Ron Kohavi , George H. John

Venue:	AIJ SPECIAL ISSUE ON RELEVANCE
Citations:	1567 - 3 self

BibTeX

@MISC{Kohavi97wrappersfor,
    author = {Ron Kohavi and George H. John},
    title = { Wrappers for Feature Subset Selection},
    year = {1997}
}

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Abstract

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 andshow a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes.

Keyphrases

feature subset selection subset selection selection problem learning algorithm induction algorithm decision tree significant improvement selection method particular learning algorithm optimal feature subset possible performance relevant subset wrapper method search improved design particular algorithm optimal feature particular training filter approach

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