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Simple generative model for assessing feature selection based on relevance, redundancy, and redundancy
Author(s): James Theiler
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Paper Abstract

An experimental procedure is proposed for measuring the performance of feature selection algorithms in a way that is not directly tied either to particular machine learning algorithms or to particular applications. The main interest is in situations for which there are a large number of features to be sifted through. The approach is based on simulated training sets with adjustable parameters that characterize the relevance" of individual features as well as the collective redundancy" of sets of features. In some cases, these training sets can be virtualized; that is, having specified their properties, one does not actually have to explicitly generate them. As a specific illustration, the method is used to compare variants of the minimum redundancy maximum relevance (mRMR) algorithm, and to characterize the performance of these variants in different regimes.

Paper Details

Date Published: 6 September 2019
PDF: 11 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390S (6 September 2019); doi: 10.1117/12.2529614
Show Author Affiliations
James Theiler, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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