Analysis of a Plurality Voting-based Combination of Classifiers

Authors:
Xiaoyan Mu;Paul Watta;Mohamad H. Hassoun
Affiliations:
Department of Electrical and Computer Engineering, Rose-Hullman Institute of Technology, Terre Haute, USA 47803;Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, USA 48128;Department of Electrical and Computer Engineering, Wayne State University, Detroit, USA 48202
Venue:
Neural Processing Letters
Year:
2009

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Abstract

In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition result. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting based ensemble classifier. Theoretical expressions for system performance are derived as a function of the model parameters: N (number of classifiers), m (number of classes), and p (probability that a single classifier is correct). Experimental results on the human face recognition problem show that the voting strategy can successfully achieve high detection and identification rates, and, simultaneously, low false acceptance rates.