A Neural Network Ensemble Model Adapted to Multi-class Problem

A new neural network ensemble model based on ensemble learning model adapted to multi-class problem is proposed. The base components of the proposed model are composed by the union of a binary classifier of OAA and a complementary multi-class classifier. Experimental results show that the model has higher accuracy than other classical ensemble algorithms for multi-class problems, and it has the superiority of less storage space and computation time.