Confusion matrix disagreement for multiple classifiers

Authors:
Cinthia O. A. Freitas;João M. De Carvalho;José Josemar Oliveira, Jr.;Simone B. K. Aires;Robert Sabourin
Affiliations:
Pontificia Universidade Católica do Paraná, Brazil;Universidade Federal de Campina Grande, Brazil;Universidade Federal de Campina Grande, Brazil;Universidade Tecnológica Federal do Paraná, Brazil;École de Technologie Supérieure, Canada
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

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Abstract

We present a methodology to analyze Multiple Classifiers Systems (MCS) performance, using the disagreement concept. The goal is to define an alternative approach to the conventional recognition rate criterion, which usually requires an exhaustive combination search. This approach defines a Distance-based Disagreement (DbD) measure using an Euclidean distance computed between confusion matrices and a soft-correlation rule to indicate the most likely candidates to the best classifiers ensemble. As case study, we apply this strategy to two different handwritten recognition systems. Experimental results indicate that the method proposed can be used as a low-cost alternative to conventional approaches.