说明：  将多类别问题分解成多个二类别问题是解决多类别分类问题的常用方式。传统one against all（OAA）分解方式的性能更多的依赖于个体分类器的精度，而不是它的差异性。本文介绍一种基于集成学习的适于多类问题的神经网络集成模型，其基本模块由一个OAA方式的二类别分类器和一个补充多类分类器组成。测试表明，该模型在多类问题上比其他经典集成算法有着更高的精度，并且有较少存储空间和计算时间的优势。
(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.)