An information-theoretic strategy for constructing multiple classifier systems

Most studies on combining multiple classifiers have mainly focused on how to combine their classification results and only a few studies have investigated on how to construct multiple classifier systems from available classifiers pool. In this paper, an information-theoretic strategy based on information theory model is proposed for constructing the multiple classifier systems. Provided that the number off classifiers in the multiple classifier systems is restricted in advance, this proposed strategy is applied to the classifiers pool and examines the possible sets of classifiers with the information-theoretic measure, and then it selects some sets of classifiers as the multiple classifier system candidates. The multiple classifier system candidates were evaluated together with the other sets of classifiers in the recognition of unconstrained handwritten numerals. The experimental results supported that the proposed strategy was a promising approach.