Probabilistic support vector machines for classification of noise affected data

Information Sciences

Volumn 221, Issue , 2013, Pages 60-71

  Li, Han Xiong ^a,b   Yang, Jing Lin ^a,c   Zhang, Geng ^b   Fan, Bi ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b CENTRAL SOUTH UNIVERSITY   (China)

^c China Southern Grid Company   (China)

Author keywords

Classification; PCA based sampling; Probabilistic distribution; SVM

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; OPTIMIZATION; PROBABILITY DISTRIBUTIONS; QUADRATIC PROGRAMMING; SEPARATION; STOCHASTIC SYSTEMS;

DECISION FUNCTIONS; MACHINE LEARNING COMMUNITIES; MACHINE-LEARNING DATABASE; PROBABILISTIC DISTRIBUTION; PROBABILISTIC INFORMATION; QUADRATIC OPTIMIZATION PROBLEMS; STOCHASTIC VARIABLE; SUPPORT VECTOR MACHINE (SVMS);

SUPPORT VECTOR MACHINES;

EID: 84868709802     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.09.041     Document Type: Article

References (47)

1. J. Bi, T. Zhang, Support vector classification with input data uncertainty, Advances in Neural Information Processing Systems 17 (2004) 161-168.
2. C.L. Blake, C.J. Merz, UCI Repository of Machine Learning Databases, 1998. 〈http://www.ics.uci.edu/-mlearn/MLRepository.html.
3. G. Bloch, F. Lauer, G. Colin, Y. Chamaillard, Support vector regression from simulation data and few experimental samples, Information Sciences 178 (2008) 3813-3827.
4. M.P.S. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares Jr., D. Haussler, Knowledge-based analysis of microarray gene expression data by using support vector machines, Proceedings of the National Academy of Science USA 97 (2000) 262-267.
5. C.J.C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery 2 (1998) 121-167.
6. S. Chakrabartty, G. Cauwenberghs, Gini-support vector machine: quadratic entropy based robust multi-class probability regression, Journal of Machine Learning Research 8 (2007) 813-839.
7. S. Chakrabartty, G. Cauwenberghs, Jayadeva, Sparse probability regression by label partitioning, in: Proc. 16th Conf. Computational Learning Theory (COLT'03), Washington DC, August 2003, pp. 24-27.
8. C.L. Philip Chen, Steven R. LeClair, Yoh-Han Pao, An incremental adaptive implementation of functional-link processing for function approximation, time-series prediction, and system identification, Neurocomputing 18 (1998) 1-13.
9. R.O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, second ed., John Wiley & Sons, Inc., New York, 2001.
10. B. Efron, Bootstrap methods: another look at the jackknife, Annals of Statistics 7 (1979) 1-26.
11. J.B. Gao, S.R. Gunn, C.J. Harris, M. Brown, A probabilistic framework for SVM regression and error bar estimation, Machine Learning 46 (2002) 71-89.
12. I. Guyon, N. Matić, V.N. Vapnik, Discovering Information Patterns and Data Cleaning, MIT Press, Cambridge, MA, 1996. pp. 181-203.
13. Q. He, C.X. Wu, Separating theorem of samples in Banach space for support vector machine learning, International Journal of Machine Learning and Cybernetics 1 (2) (2011) 49-54.
14. Y. Hong, H.X. Li, Comparative study of fluid dispensing modelling, IEEE Transactions on Electronics Packaging Manufacturing 26 (2003) 273-280.
15. H.P. Huang, Y.H. Liu, Fuzzy support vector machines for pattern recognition and data mining, International Journal of Fuzzy Systems 4 (2002) 826-835.
16. T. Jebara, R. Kondor, A. Howark, Probability product kernels, Journal of Machine Learning Research 5 (2004) 819-844.
17. B. Jin, Y.C. Tang, Y.Q. Zhang, Support vector machines with genetic fuzzy feature transformation for biomedical data classification, Information Sciences 177 (2007) 476-489.
18. M.A. Kumar, R. Khemchandani, M. Gopal, S. Chandra, Knowledge based least squares twin support vector machines, Information Sciences 180 (23) (2010) 4606-4618.
19. J.T.Y. Kwok, The evidence framework applied to support vector machines, IEEE Transactions on Neural Networks 11 (2000) 1162-1173.
20. Y.J. Lee, S.Y. Huang, Reduced support vector machines: a statistical theory, IEEE Transactions on Neural Networks 18 (2007) 1-13.
21. H.X. Li, H. Deng, J. Zhong, Model-based integration of control and supervision for one kind of curing process, IEEE Transactions on Electronics Packaging Manufacturing 27 (2004) 177-186.
22. H.X. Li, Z. Liu, A probabilistic neural-fuzzy learning system for stochastic modelling, IEEE Transactions on Fuzzy Systems 16 (2008) 898-907.
23. C.A.M. Lima, A.L.V. Coelho, F.J. Von Zuben, Hybridizing mixtures of experts with support vector machines: investigation into nonlinear dynamic systems identification, Information Sciences 177 (2007) 2049-2074.
24. C.F. Lin, S.D. Wang, Fuzzy support vector machine, IEEE Transactions on Neural Networks 13 (2002) 464-471.
25. W.Y. Liu, K. Yue, M.H. Gao, Constructing probabilistic graphical model from predicate formulas for fusing logical and probabilistic knowledge, Information Sciences 181 (18) (2011) 3828-3845.
26. Z. Liu, Q.H. Wu, Y. Zhang, C.L. Philip Chen, Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery, International Journal of Machine Learning and Cybernetics 1 (2) (2011) 37-47.
27. Z. Liu, H.X. Li, A probabilistic fuzzy logic system for modeling and control, IEEE Transactions on Fuzzy Systems 13 (2005) 848-859.
28. Z. Liu, H.X. Li, Y. Zhang, Stochastic and incomplete data-based modeling with probabilistic wavelet system, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 28 (2008) 310-319.
29. S. Maldonado, R. Weber, J. Basak, Simultaneous feature selection and classification using kernel-penalized support vector machines, Information Sciences 181 (1) (2011) 115-128.
30. J.C. Platt, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Advances in Large Margin Classifiers, MIT Press, 1999.
31. N. Pochet, F.D. Smet, J.A.K. Suykens, B.L.R.D. Moor, Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction, Bioinformatics 20 (2004) 3185-3195.
32. P. Sollich, Bayesian methods for support vector machines: evidence and predictive class probabilities, Machine Learning 46 (2002) 21-52.
33. J.A.K. Suykens, B. De Moor, Optimal control by least squares support vector machines, Neural Networks 14 (2001) 23-35.
34. V.N. Vapnik, Statistical Learning Theory, John Wiley, New York, 1998.
35. X.Z. Wang, C.R. Dong, Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy, IEEE Transactions on Fuzzy Systems 17 (3) (2009) 556-567.
36. X.Z. Wang, J.H. Zhai, S.X. Lu, Induction of multiple fuzzy decision trees based on rough set technique, Information Sciences 178 (16) (2008) 3188-3202.
37. X.Z. Wang, S.X. Lu, J.H. Zhai, Fast fuzzy multi-category SVM based on support vector domain description, International Journal of Pattern Recognition and Artificial Intelligences 22 (1) (2008) 09-120.
38. Y.Q. Wang, S.Y. Wang, K.K. Lai, A new fuzzy support vector machine to evaluate credit risk, IEEE Transactions on Fuzzy Systems 13 (2005) 820-831.
39. L.W. Wei, Z.Y. Chen, J.P. Li, Evolution strategies based adaptive Lp LS-SVM, Information Sciences 181 (14) (2011) 3000-3016.
40. R. Xu, D. Wunsch II, Survey of clustering algorithms, IEEE Transactions on Neural Networks 16 (2005) 645-678.
41. J.L. Yang, Han-Xiong Li, A probabilistic support vector machine for uncertain data, in: CIMSA 2009 - International Conference on Computational Intelligence for Measurement Systems and Applications, Hong Kong, China, May 11-13, 2009, pp. 163-168.
42. J.Q. Yang, S. Gunn, Exploiting uncertain data in support vector classification, in: 11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems, Vietri sul Mare, Italy, September 12-14, 2007, pp. 148-155.
43. J.L. Yang, H.X. Li, Y. Hu, A probabilistic SVM based decision system for pain diagnosis, Expert Systems With Applications 38 (2011) 9346-9351.
44. S.M. Zhang, P. McCullagh, C. Nugent, H.R. Zheng, M. Baumgarten, Optimal model selection for posture recognition in home-based healthcare, International Journal of Machine Learning and Cybernetics 1 (2) (2011).
45. X. Zhang, Using class-center vectors to build support vector machines, in: Proceedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing, vol. IX, 1999, pp. 3-11.
46. J. Zhu, T. Hastie, Kernel logistic regression and the import vector machine, Journal of Computational and Graphical Statistics 14 (1) (2005) 185-205.
47. A.M. Zoubir, B. Boashash, The bootstrap and its application in signal processing, IEEE Signal Processing Magazine 15 (1998) 56-76.