Classifier-Independent Feature Selection Based on Non-parametric Discriminant Analysis

Authors:
Naoto Abe;Mineichi Kudo;Masaru Shimbo
Affiliations:
-;-;-
Venue:
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2002

Citing 4
Cited 0

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Sequential selection of discrete features for neural networks – A Bayesian approach to building a cascade

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A Divergence Criterion for Classifier-Independent Feature Selection

Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition

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Abstract

A novel algorithm for classifier-independent feature selection is proposed. There are two possible ways to select features that are effective for any kind of classifier. One way is to correctly estimate the class-conditional probability densities and the other way is to accurately estimate the discrimination boundary. The purpose of this study is to find the discrimination boundary and to determine the effectiveness of features in terms of normal vectors along the boundary. The fundamental effectiveness of this approach was confirmed by the results of several experiments.