Multiple classifiers by constrained minimization
The paper describes an approach to combining multiple classifiers in order to improve classification accuracy. Since individual classifiers in the ensemble should somehow be uncorrelated to yield higher classification accuracy than a single classifier, we propose to train classifiers by minimizing the correlation between their classification errors. A simple combination strategy for three classifiers is then proposed and its achievable error rate is analyzed and compared to individual single classifier performance. The proposed approach has been evaluated on artificial data and a nasal/oral vowel classification task. Theoretical analyses and experimental results illustrate the effectiveness of the proposed approach.