Publication Date

5-3-1997

Technical Report Number

TR97-01

Subjects

Theory of Computation, Software

Abstract

Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This paper presents an approach to the multi-criteria optimization problem of feature subset selection using a genetic algorithm. Our experiments demonstrate the feasibility of this approach for feature subset selection in automated design of neural networks for pattern classification and knowledge discovery.

Share

COinS