A measure of competence based on random classification for dynamic ensemble selection

Authors:
Tomasz Woloszynski;Marek Kurzynski;Pawel Podsiadlo;Gwidon W. Stachowiak
Affiliations:
Tribology Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Crawley, Western Australia 6009, Australia;Chair of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland;Tribology Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Crawley, Western Australia 6009, Australia;Tribology Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Crawley, Western Australia 6009, Australia
Venue:
Information Fusion
Year:
2012

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Abstract

In this paper, a measure of competence based on random classification (MCR) for classifier ensembles is presented. The measure selects dynamically (i.e. for each test example) a subset of classifiers from the ensemble that perform better than a random classifier. Therefore, weak (incompetent) classifiers that would adversely affect the performance of a classification system are eliminated. When all classifiers in the ensemble are evaluated as incompetent, the classification accuracy of the system can be increased by using the random classifier instead. Theoretical justification for using the measure with the majority voting rule is given. Two MCR based systems were developed and their performance was compared against six multiple classifier systems using data sets taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The systems developed had typically the highest classification accuracies regardless of the ensemble type used (homogeneous or heterogeneous).