A new ensemble diversity measure applied to thinning ensembles

Authors:
Robert E. Banfield;Lawrence O. Hall;Kevin W. Bowyer;W. Philip Kegelmeyer
Affiliations:
Department of Computer Science and Engineering, University of South Florida, Tampa, Florida;Department of Computer Science and Engineering, University of South Florida, Tampa, Florida;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Sandia National Labs, Biosystems Research Department, Livermore, CA
Venue:
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Year:
2003

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Abstract

We introduce a new way of describing the diversity of an ensemble of classifiers, the Percentage Correct Diversity Measure, and compare it against existing methods. We then introduce two new methods for removing classifiers from an ensemble based on diversity calculations. Empirical results for twelve datasets from the UC Irvine repository show that diversity is generally modeled by our measure and ensembles can be made smaller without loss in accuracy.