Validation of relative feature importance using natural data

Authors:
Hilary J. Holz;Murray H. Loew
Affiliations:
Department of Mathematics and Computer Science, California State University, Hayward, CA;Department of Electrical and Computer Engineering, The George Washington University, Washington, DC
Venue:
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Year:
2002

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Abstract

Feature analysis for classification is based on the discriminatory power of features. In previous research, we have presented a metric called relative feature importance (RFI) for measuring the non-parametric discriminatory power (NPDP) of features. RFI has been shown to correctly rank features for a variety of artificial data sets. In this work, we validate RFI on natural data, using several natural data sets.