MULTIPLE CLASSIFIER ERROR PROBABILITY FOR MULTI-CLASS PROBLEMS

In this paper we consider majority voting of multiple classifiers systems in the case of two-valued decision support for many-class problem. Using an explicit representation of the classification error probability for ensemble binomial voting and two class problem, we obtain general equation for classification error probability for the case under consideration. Thus we are extending theoretical analysis of the given subject initially performed for the two class problem by Hassen and Salamon and still used by Kuncheva and other researchers. This allows us to observe important dependence of maximal posterior error probability of base classifier allowable for building multiple classifiers from the number of considered classes. This indicates the possibility of improving the performance of multiple classifiers for multiclass problems, which may have important implications for their future applications in many fields of science and industry, including the problems of machines diagnostic and systems reliability testing.