Features Selection based on Rough Membership and Genetic Programming

Abstract

This paper discusses the feature selection problem upon supervised learning. A learning method based on rough sets and genetic programming is proposed to select significant features and classify numerical data. The proposed method uses rough membership to transform nominal data into numerical values, then selects important features and learns classification… (More)
DOI: 10.1109/ICSMC.2006.384780

8 Figures and Tables

Topics

  • Presentations referencing similar topics