Decompose multi-class problem into multiple binary class problems is a common way to solve multi-class problem. The performance of the traditional one against all (OAA) decomposition way mainly depends on the accuracy of individual classifiers, not their diversity. In this paper, a new ensemble learning model applicable to multiclass domains is proposed. The proposed model is a neural network ensemble in which the base learners are composed by the union of a binary classifier and a complement multi-class classifier. Experimental results show that our model has higher accuracy than other classical ensemble learning for multi-class problems. And it has the superiority with less storage space and computation time.

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