On naive Bayesian fusion of dependent classifiers

In classifier combination, the relative values of a posteriori probabilities assigned to different hypotheses are more important than the accuracy of their estimates. Because of this, the independence requirement in naive Bayesian fusion should be examined from combined accuracy point of view. In this study, it is investigated whether there is a set of dependent classifiers which provides a better combined accuracy than independent classifiers when naive Bayesian fusion is used. For this purpose, two classes and three classifiers case is initially considered where the pattern classes are not equally probable. Taking into account the increased complexity in formulations, equal a priori probabilities are considered in the general case where N classes and K classifiers are used. The analysis carried out has shown that the combination of dependent classifiers using naive Bayesian fusion may provide much better combined accuracies compared to independent classifiers.