Combining classifiers based on minimization of a Bayes error rate

In order to raise a class discrimination power by combining multiple classifiers, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables should be minimized. Wang and Wong (1979) proposed a tree dependence approximation scheme of a high order probability distribution composed of those variables, based on minimizing the upper bound. In addition to that, this paper presents an extended approximation scheme dealing with higher order dependency. Multiple classifiers recognizing unconstrained handwritten numerals were combined by the proposed approximation scheme based on the minimization of the Bayes error rate, and the high recognition rates were obtained by them.