Generating Classifier Outputs with Fixed Diversity for Evaluating Voting Methods
Recently, it has been shown that for majority voting combination methods, (negatively) dependent classifiers may provide better performance compared to that obtained with independent classifiers. The aim of this paper is to analyse the performance of plurality voting according to classifier diversity (agreement). This analysis is conducted in parallel with majority voting in order to show which method is more efficient with dependent classifiers. For this purpose, we develop a new method for the artificial generation of classifier outputs with fixed individual accuracies and pair-wise agreement. A diversity measure is applied for building the classifier teams. The experimental results show that the plurality voting is less sensitive to the correlation between classifiers than majority voting. It is also more efficient in achieving the trade- off between the recognition rate and rejection rate than the majority voting.
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