A game theoretical analysis of combining classifiers for multi-class classification problems

抄録

Combining binary classifiers for multi-class classification problems has been very popular after the invention of SVM and ada-boost, which are known to be very effective for binary classification. In this pater, we analyze theoretically the ECOC approach, which is a standard combining method. We discuss the problelm of combinig binary classifiers form the game-theoretical point of view. First, we develop a genaral theorem for the condition of minimaxity, which is closely related to the network flow theory. Applying this theorem, we show that the ECOC approach has the minimax property in the one-vs-one and one-vs-all case.