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Individual classification models have recently been challenged by ensemble of classifiers, also known as multiple classifier system, which often shows better classification accuracy. In terms of merging the outputs of an ensemble of classifiers, classifier selection has not attracted as much attention as classifier fusion in the past, mainly because of its higher computational burden. In this paper, we propose a novel technique for improving classifier selection. In our method, the simple divide-and-conquer strategy is adapted in that a complex classification problem is divided into simpler binary sub-classification problems. We conduct extensive experiments on a series of multi-class datasets from the UCI (University of California, Irvine) repository and on odor database. The experimental results demonstrate the advanced performance of the proposed method.

Keywords: classification, multi-class, ensemble system, classifier selection, odor recognition.

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