【摘要】 针对多类分类问题,研究支持向量机集成中的分类器组合架构与方法.分析已有的多类级和两类级支持向量机集成架构的不足后,提出两层的集成架构.在此基础上,研究基于证据理论的支持向量机度量层输出信息融合方法,针对一对多与一对一两种多类扩展策略,分别定义基本概率分配函数,并根据证据冲突程度采用不同的证据组合规则.在一对多策略下,采用经典的Dempster规则;在一对一策略下则提出一条新的规则,以组合冲突严重的证据.实验表明,两层架构优于多类级架构,证据理论方法能有效地利用两类支持向量机的度量层输出信息,取得了满意的结果.更多还原

【Abstract】 Ensemble learning has become a main research topic in the field of machine learning recently.By training and combining some accurate and diverse classifiers,ensemble learning provides a novel approach for improving the generalization performance of classification systems.Studied in this paper are the architectures and methods for combination of multiple classifiers in support vector machine(SVM) ensemble for multi-class classification.After analyzing the defects of the known architectures including multi-class-level SVM ensemble and binary-class-level SVM ensemble,a two-layer architecture is proposed to construct SVM ensemble.Then fusion methods of the measurement-level output information of SVMs are studied based on the evidence theory.Different basic probability assignment functions are defined respectively in terms of the used strategy for multi-class extension,i.e.one-against-all and one-against-one,and different evidence combination rules are adopted according to the degree of conflicts among evidence.In the case of one-against-all strategy,the classical Dempster’s rule can be used while in the case of one-against-one strategy a new rule is proposed to combine the heavily conflicting evidence.The experimental results show that the two-layer architecture is better than the multi-class-level architecture.Moreover,the evidence theory based methods can effectively utilize the measurement-level output information of binary SVMs so as to gain satisfactory classification accuracies.更多还原