A systematic approach to classifier selection on combining multiple classifiers for handwritten digit recognition

Much research has been done on combining multiple classifiers for handwritten character recognition to improve the performance of the classifier. Given a fixed set of classifiers using the same or different kinds of feature set, they focus on a methodology to combine all of the classifiers. In this paper, given a variable set of classifiers, we focus on a methodology to determine which subset of classifiers achieves the optimal combination results. In order to evaluate the dependency between classifiers, we propose a similarity measure between them which can be calculated from the errors generated by each classifier. This similarity measure allows us to compare the performance of one combination case relative to those of the other cases without performing any experiments. Using five individual neural net classifiers with different feature sets [gradient, structural, UDLRH (up-down left-right hole), mesh and LSF (large stroke feature)], we perform handwritten digit recognition experiments. With three combination methods [majority voting, Borda count and LCA (linear confidence accumulation)], we perform combination experiments for all possible cases of three classifiers selected from among the above five. Then, we compare their rankings in terms of the recognition rate with that in terms of the similarity measure. This comparison shows the effectiveness of the proposed method