On the Combination of Locally Optimal Pairwise Classifiers

Classification methods generally rely on some idea about the data structure. If the specific assumptions are not met, a classifier may fail. In this paper the possibility of combining classifiers in multi-class problems is investigated. Multi-class classification problems are split into two class problems. For each of the latter problems an optimal classifier is determined. The results of applying the optimal classifiers on the two class problems can be combined using a pairwise coupling algorithm. In this paper exemplary situations are investigated where the respective assumptions of Naive Bayes or the classical Linear Discriminant Analysis (LDA) fail. It is investigated at which degree of violations of the assumptions it may be advantageous to use single methods or a classifier combination by pairwise coupling.