Comparison of classifier selection methods for improving committee performance

Authors:
Matti Aksela
Affiliations:
Helsinki University of Technology, Neural Networks Research Centre, Finland
Venue:
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Year:
2003

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Abstract

Combining classifiers is an effective way of improving classification performance. In many situations it is possible to construct several classifiers with different characteristics. Selecting the member classifiers with the best individual performance can be shown to be suboptimal in several cases, and hence there exists a need to attempt to find effective member classifier selection methods. In this paper six selection criteria are discussed and evaluated in the setting of combining classifiers for isolated handwritten character recognition. A criterion focused on penalizing many classifiers making the same error, the exponential error count, is found to be able to produce the best selections.