Computer Science ›› 2016, Vol. 43 ›› Issue (5): 252-256.doi: 10.11896/j.issn.1002-137X.2016.05.047

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Selective Ensemble of SVDDs Based on Correntropy and Distance Variance

XING Hong-jie and WEI Yong-le   

  • Online:2018-12-01 Published:2018-12-01

Abstract: 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.

Key words: One-class classification,Support vector data description,Correntropy,Selective ensemble

[1] Tax D M J.One-class classification:concept learning in the absence of counter examples[D].Delft University of Technology,2001
[2] Schlkopf B,Williamson R C,Smola A J,et al.Support vectormethod for novelty detection[C]∥Conference:Advances in Neural Information Processing Systems.2000:582-588
[3] Tax D M J,Duin R P W.Support vector data description[J].Machine Learning,2004,54(1):45-66
[4] Tax D M J,Duin R P W.Combining one-class classifiers[C]∥Proceedings of the 2nd International Workshop on Multiple Classifier Systems.2001:299-308
[5] Seguí S,Igual L,Vitrià J.Weighted bagging for graph based one-class classifiers[C]∥Proceedings of the 9th International Workshop on Multiple Classifier Systems.2010:1-10
[6] Zhang J,Lu J,Zhang G.Combining one class classification mo-dels for avian influenza outbreaks[C]∥Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making.2011:190-196
[7] Wilk T,Wozniak M.Soft computing methods applied to combination of one-class classifiers[J].Neurocomputing,2012,75:185-193
[8] Casale P,Pujol O,Radeva P.Approximate polytope ensemble for one-class classification[J].Pattern Recognition,2014,47:854-864
[9] Krawczyk B,Wo′niak M,Cyganek B.Clustering-based ensembles for one-class classification[J].Information Sciences,2014,264:182-195
[10] Zhou Z H,Wu J X,Tang W.Ensembling neural networks:many could be better than all[J].Artificial Intelligence,2002,137(1/2):239-263
[11] Wang L,Li Q.Effective selective ensemble algorithms for support vector machines[J].Neurocomputing,2010:287-295
[12] Li N,Zhou Z.Selective ensemble under regularization frame-work[J].Lecture Notes in Computer Science,2009,5519:293-303
[13] Zhang L,Zhou W.Sparse ensembles using weighted combination methods based on linear programming[J].Pattern Recognition,2011,44(1):97-106
[14] Krawczyk B.One-class classifier ensemble pruning and weighting with firefly algorithm[J].Neurocomputing,2015,150(B):490-500
[15] Vapnik V N.Statistical Learning Theory[M].New York:Wiley,1998
[16] Principe J C.Information Theory Learning:Renyi’s Entropy and Kernel Perspectives[M].Springer,2012
[17] Tang E K,Suganthan P N,Yao X.An analysis of diversitymeasures[J].Machine Learning,2006,65(1):247-271
[18] Yin X C,Huang K,Yang C,et al.Convex ensemble learning with sparsity and diversity[J].Information Fusion,2014,20:49-59
[19] Vedelsby J,Krogh A.Neural network ensembles,cross-validation and active learning[J].Advances in Neural Information Processing Systems,1995,7:231-238
[20] He R,Hu B G,Zheng W S,et al.Robust principal componentanalysis based on maximum correntropy criterion[J].IEEE Transactions on Image Processing,2011,20(6):1485-1494
[21] Eockfellar R.Convex analysis[M].Princeton University Press,Princeton,1970
[22] Wu M,Ye J.A small sphere and large margin approach for no-velty detection using training data with outliers[J].IEEE Tran-sactions on Pattern Analysis and Machine Intelligence,2009,31(11):2088-2092
[23] Frank A,Asuncion A.UCI machine learning repository.University of California,Irvine,School of Information and Computer Science,Irvine,CA.

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