%A XING Hong-jie and WEI Yong-le %T Selective Ensemble of SVDDs Based on Correntropy and Distance Variance %0 Journal Article %D 2016 %J Computer Science %R 10.11896/j.issn.1002-137X.2016.05.047 %P 252-256 %V 43 %N 5 %U {https://www.jsjkx.com/CN/abstract/article_15986.shtml} %8 2018-12-01 %X Selective ensemble of support vector data description(SVDD) based on correntropy of information theoretic learning and distance variance was proposed.Correntropy is utilized to substitute mean square error to measure the compactness of ensemble and construct more compact classification boundary.Distance variance is used to measure the diversity of base classifiers to enhance the diversity of the ensemble model.An 1 norm based regularization term is introduced into the objective function to implement the selective ensemble.Moreover,the half-quadratic optimization technique is utilized to solve the proposed selective ensemble model.In comparison with single SVDD,Bagging based ensemble of SVDDs,and AdaBoost based ensemble of SVDDs,the proposed method achieves better classification perfor-mance.