Feature Extraction, Construction and Selection

Abstract

Choosing the best method for feature selection depends on the extent of a-priori knowledge of the problem. We present two basic approaches. One involves computationally effective floating-search methods; the other trades off the requirement for a-priori information for the requirement of sufficient data to represent the distributions involved. We've developed methods for statistical pattern recognition that, based on the user's level of knowledge of a problem, can reduce the problem's dimensionality. We believe that these methods can enrich the methodology of subset selection for other fields of AI. This article provides an overview of our methods and techniques. focusing on the basic principles and their potential use