On the Effectiveness of Diversity When Training Multiple Classifier Systems

Authors:
David Gacquer;Véronique Delcroix;François Delmotte;Sylvain Piechowiak
Affiliations:
Univ Lille Nord de France, F-59000 Lille,France UVHC, LAMIH, Valenciennes, France F-59313;Univ Lille Nord de France, F-59000 Lille,France UVHC, LAMIH, Valenciennes, France F-59313;Univ Lille Nord de France, F-59000 Lille,France UVHC, LAMIH, Valenciennes, France F-59313;Univ Lille Nord de France, F-59000 Lille,France UVHC, LAMIH, Valenciennes, France F-59313
Venue:
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2009

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Abstract

Discussions about the trade-off between accuracy and diversity when designing Multiple Classifier Systems is an active topic in Machine Learning. One possible way of considering the design of Multiple Classifier Systems is to select the ensemble members from a large pool of classifiers focusing on predefined criteria, which is known as the Overproduce and Choose paradigm. In this paper, a genetic algorithm is proposed to design Multiple Classifier Systems under this paradigm while controlling the trade-off between accuracy and diversity of the ensemble members. The proposed algorithm is compared with several classifier selection methods from the literature on different UCI Repository datasets. This paper specifies several conditions for which it is worth using diversity during the design stage of Multiple Classifier Systems.