Creating and measuring diversity in multiple classifier systems using support vector data description

Applied Soft Computing Journal

Volumn 11, Issue 8, 2011, Pages 4931-4942

  Haghighi, Mehdi Salkhordeh ^a   Vahedian, Abedin ^a   Yazdi, Hadi Sadoghi ^a  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Classifier ensemble; Classifier fusion; MCS diversity; Multiple classifier system; Support vector data descriptor

Indexed keywords

CLASSIFIER ENSEMBLES; CLASSIFIER FUSION; MCS DIVERSITY; MULTIPLE CLASSIFIER SYSTEM; SUPPORT VECTOR DATA DESCRIPTOR;

DATA DESCRIPTION;

EID: 80053576495     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2011.06.006     Document Type: Article

References (50)

1. D. Ruta, and G. Bogdan An overview of classifier fusion methods Computing and Information Systems 7 2000 1 10
2. L.I. Kuncheva, and C.J. Whitaker Measures of diversity in classifier ensembles Machine Learning 51 2003 181 207
3. M. Aksela, and J. Laaksonen Using diversity of errors for selecting members of a committee classifier Pattern Recognition 39 2006 608 623 (Pubitemid 43199459)
4. G. Giancinto, and F. Roli Dynamic classifier selection based on multiple classifie rbehaviour Pattern Recognition 34 9 2001 1879 1881
5. K. Woods, K. Kegelmeyer, and K. Bowyer Combination of multiple classifiers using local accuracy estimates IEEE Transactions on Pattern Analysis and Machine Intelligence 19 4 1997 405 410 (Pubitemid 127762896)
6. J. Kittler On combining classifiers IEEE Transactions on Pattern Analysis and Machine Intelligence 20 3 1998 226 239 (Pubitemid 128741312)
7. Kuncheva, and I. Ludmila Combining Pattern Classifiers Methods and Algorithms 2004 John Wiley & Sons, Inc. New Jersey
8. L.I. Kuncheva Switching between selection and fusion in combining classifiers: an experiment IEEE Transactions on Systems Man and Cybernetics 2 32 2002 146 156 (Pubitemid 34249373)
9. L.I. Kuncheva, and J.C. Bezdek Decision templates for multiple classifier fusion: an experimental comparison Pattern Recognition 2001 299 314 (Pubitemid 32871876)
10. L. Xu, A. Krzyzk, and C.Y. Suen Methods of combining multiple classifiers and their application to handwriting recognition IEEE Transactions on Systems Man and Cybernetics 3 22 1992 418 435
11. M.R. Ahmadzadeh, and M. Petrou Use of Dempster-Shafer theory to combine classifiers which use different class boundaries Pattern Analysis and Applications 6 2003 41 46 (Pubitemid 36719923)
12. S.B. Cho, and J.H. Kim Combining multiple neural networks by fuzzy integral and robust classification IEEE Transactions on Systems Man and Cybernetics 1995 380 384
13. Y.S. Huang, and C.Y. Suen A method of combining multiple experts for the recognition o funconstrained handwritten numerals IEEE Transactions on Pattern Analysis and Machine Intelligence 17 1 1995 90 94
14. Y. Freund, and R.E. Schapire Experiments with a new boosting algorithm 13th International Conference on Machine Learning 1996 48 156
15. Y. Freund, and R.E. Schapire A decision - theoretic generalization of on-line learning and an application to boosting 55 1 1997 119 139
16. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140 (Pubitemid 126724382)
17. M. Kamel, and N. Wanas Data dependence in combining classifiers, in MCS Lecture Notes in Computer Science 2709 2003 1 14
18. N.M. Wanas, R.A. Dara, and M.S. Kamel Adaptive fusion and co-operative training for classifie rensembles Pattern Recognition 39 2006 1781 1794 (Pubitemid 43867570)
19. H. Zouari, L. Heutte, and Y. Lecourtier Controlling the diversity in classifier ensembles through a measure of agreement Pattern Recognition 38 2005 2195 2199 (Pubitemid 41214656)
20. C.A. Shipp, and L.I. Kuncheva An investigation into how adaboost affects classifier diversity IPMU 2002 203 208
21. C.A. Shipp, and L.I. Kuncheva Relationships between combination methods and measures of diversity in combining classifiers Information Fusion 3 2 2002 135 148
22. L.K. Hansen, and P. Salamon Neural network ensembles IEEE Transactions on Pattern Analysis and Machine Intelligence 12 10 1990 993 1001
23. K. Turner, and J. Ghosh Error correlation and error reduction in ensemble classifiers Connection Science 8 3 1996 385 403
24. T. Dietterich An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization Machine Learning 40 2000 139 158
25. S. Geman, E. Bienenstock, and R. Doursat Neural networks and the bias/variance dilemma Neural Computation 4 1 1992 1 58
26. P. Cunningham, J. Carney, Diversity versus quality in classification ensembles based on feature selection. Department of Computer Science, Trinity College, Dublin: s.n., 2000. Technical Report. TCD-CS-2000-02.
27. R. Kohavi, and D.H. Wolpert Bias plus variance decomposition for zero-one loss functions 13th International Conference on Machine Learning 1996 275 283
28. A. Krogh, and J. Vedelsby Neural network ensembles, cross validation and active learning Advances in Neural Information Processing Systems 7 1995 231 238
29. Z. Kong, and Z. Cai Advances of research in fuzzy integral for classifier's fusion 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2007 809 814
30. D. Partridge, and W.J. Krzanowski Software diversity: practical statistics for its measurement and exploitation Information & Software Technology 39 1997 707 717 (Pubitemid 127403015)
31. Sharkey Combining artificial neural nets: ensemble and modular multi-net systems 1999 Multi-Net Systems, Springer-Verlag pp. 1-30
32. J.J. Rodriguez, and L.I. Kuncheva Rotation forest: a new classifier ensemble method IEEE Transactions on Pattern Analysis and Machine Intelligence 28 10. 2006
33. L.I. Kuncheva, and J.J. Rodríguez Random subspace ensembles for fMRI classification IEEE Transactions on Medical Imaging 29 February (2) 2010
34. R.P.W. Duin, and D.M.J. Tax Experiments with classifier combining rules International Workshop on Multiple Classifier Systems, LNCS Calgiari, Italy: s.n. 2000 16 29
35. D. Partridge, and W.B. Yates Engineering multiversion neural-net systems Neural Computation 8 4 1996 869 893 (Pubitemid 126449950)
36. L. Chen, and M.S. Kamel A generalized adaptive ensemble generation and aggregation approach fo rmultiple classifier systems Pattern Recognition 42 2009 629 644
37. M. Aksela Comparison of classifier selection methods for improving committee performance MCS 2003 2003 84 93
38. D. Tax, and R. Duin Support vector domain description Pattern Recognition Letters 54 1999 1191 1199 (Pubitemid 32261897)
39. Tax David, One-class classification, 2001.
40. M.J. Tax David, and P.W. Duin Robert Support vector data description Machine Learning 54 2004 45 66
41. S.M. Guo, L.C. Chen, and J.S.H. Tsai A boundary method for outlier detection based on support vector domain description Pattern Recognition 2009 77 83
42. Chi-Kai Wang A novel approach to generate artificial outliers for support vector data description IEEE International Symposium on Industrial Electronics (ISlE 2009) Seoul Olympic Parktel, Seoul, Korea: s.n. 2009
43. Y. Liu, J.A. Starzyk, and Z. Zhu Optimizing number of hidden neurons in neural networks AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: Artificial Intelligence and Applications 2007 ACTA Press Anaheim, CA, USA
44. A. Blum Neural Networks in C++ 1992 Wiley NY
45. K. Swingler Applying Neural Networks: A Practical Guide 1996 Academic Press London
46. M.J.A. Berry, and G. Linoff Data Mining Techniques 1997 John Wiley & Sons NY
47. D.L. Chester Why two hidden layers are better than one IJCNN. vol. 1 Wash-DC: s.n. 1990 265 268
48. M.E.H. Pedersen, and A.J. Chipperfield Simplifying particle swarm optimization Applied Soft Computing 10 2010 618 628
49. C.L. Blake, C.J. Merz, UCI repository of machine learning databases. Department of Information and Computer Sciences, University of California. Irvine, s.n., http://www.ics.uci.edu/∼mlearn/MLRepository.html.
50. S. Li, and M. Tan Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm Neurocomputing 73 2010 2089 2096