WIT Press

Experimental Feature Selection Using The Wrapper Approach

Price

Free (open access)

Paper DOI

10.2495/DATA980101

Volume

22

Pages

10

Published

1998

Size

936 kb

Author(s)

J.A. Baranauskas & M.C. Monard

Abstract

Machine learning methods provide algorithms for mining databases in or- der to help analyze the information, find patterns, and improve prediction accuracy. In practice, the user of a data mining tool is interested in accu- racy, efficiency, and comprehensibility for a specific domain which may be reached through feature selection. In this work we use the wrapper approach for Feature Subset Selection. The FSS algorithm from MCC++ library was used to run experiments with datasets containing many features. Accuracies for five inducers using all features, features found by FSS as well as the union of all those selected features are presented. Results confirm the superiority of FSS wrapper approach but in some

Keywords