Novel selective support vector machine ensemble learning algorithm

Focusing on the problem that conventional ensemble learning methods may be invalid when support vector machine (SVM) is used as component learner, a new selective SVM ensemble algorithm is proposed. ξαestimator is used to estimate the generalization performance of the component SVM, and negative learning theory is used to introduce diversity among component SVMs. A set of component SVMs with high generalization performance and high diversity is selected during ensemble through recursive elimination algorithm. Experimental results on UCI data sets show that compared with single SVM, conventional Bagging ensemble method and negative learning ensemble method, the classification accuracy of selective SVM ensemble is increased on average by 0.4%, 0.24% and 0.16%, respectively.