“Good” and “bad” diversity in majority vote ensembles

Authors:
Gavin Brown;Ludmila I. Kuncheva
Affiliations:
School of Computer Science, University of Manchester, UK;School of Computer Science, Bangor University, UK
Venue:
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Year:
2010

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Abstract

Although diversity in classifier ensembles is desirable, its relationship with the ensemble accuracy is not straightforward. Here we derive a decomposition of the majority vote error into three terms: average individual accuracy, “good” diversity and “bad diversity”. The good diversity term is taken out of the individual error whereas the bad diversity term is added to it. We relate the two diversity terms to the majority vote limits defined previously (the patterns of success and failure). A simulation study demonstrates how the proposed decomposition can be used to gain insights about majority vote classifier ensembles.