Using clustering analysis to improve semi-supervised classification

Neurocomputing

Volumn 101, Issue , 2013, Pages 290-298

  Gan, Haitao ^a   Sang, Nong ^a   Huang, Rui ^a   Tong, Xiaojun ^b   Dan, Zhiping ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b WUHAN TEXTILE UNIVERSITY   (China)

Author keywords

Fuzzy c means; Self training; Semi supervised classification; Semi supervised clustering; Support vector machine

Indexed keywords

CLUSTERING ANALYSIS; DATA SPACE; FUZZY C MEAN; FUZZY C-MEANS ALGORITHMS; REAL DATA SETS; SELF-TRAINING; SEMI-SUPERVISED; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED CLUSTERING; SEMI-SUPERVISED LEARNING; SUPERVISED CLASSIFICATION; UNLABELED DATA;

CLUSTERING ALGORITHMS; COPYING; FUZZY CLUSTERING; SUPPORT VECTOR MACHINES;

SUPERVISED LEARNING;

ARTICLE; CLASSIFICATION ALGORITHM; CLUSTER ANALYSIS; DATA ANALYSIS; FUZZY SYSTEM; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

EID: 84868624200     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.08.020     Document Type: Article

References (35)

1. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2000, Wiley. second ed.
2. H. Grabner, C. Leistner, H. Bischof, Semi-supervised on-line boosting for robust tracking, in: Proceedings of the 10th European Conference on Computer Vision, 2008.
3. Su Y., Shan S., Chen X., Gao W. Hierarchical ensemble of global and local classifiers for face recognition. IEEE Trans. Image Process. 2009, 18(8):1885-1896.
4. Cao Y., He H., Huang H. LIFT. A new framework of learning from testing data for face recognition. Neurocomputing 2011, 74:916-929.
5. Pan F., Wang J., Lin X. Local margin based semi-supervised discriminant embedding for visual recognition. Neurocomputing 2011, 74:812-819.
6. Qi Z., Xu Y., Wang L., Song Y. Online multiple instance boosting for object detection. Neurocomputing 2011, 74:1769-1775.
7. C. Rosenberg, M. Hebert, H. Schneiderman, Semi-supervised Self-training of object detection models, in: Proceedings of the Seventh IEEE Workshops Application of Computer Vision, 2005, pp. 29-36.
8. A. Blum, T.M. Mitchell, Combining labeled and unlabeled data with Co-training, in: Proceedings of the 11th Annual Conference Learning Theory, 1998, pp. 92-100.
9. Yaslan Y., Cataltepe Z. Co-training with relevant random subspaces. Neurocomputing 2010, 73:1652-1661.
10. T. Joachims, Transductive inference for text classification using support vector machines, in: Proceedings of the 16th International Conference Machine Learning, 1999, pp. 200-209.
11. K. Nigam, Using Unlabeled Data to Improve Text Classification, Ph.D. Dissertation, Department of Computer Science, Carnegie Mellon University, 2001.
12. X. Zhu, Z. Ghahramani, J. Lafferty, Semi-supervised learning using Gaussian fields and harmonic functions, in: The 20th International Conference Machine Learning, 2003, pp. 912-919.
13. Zhao H. Combining labeled and unlabeled data with graph embedding. Neurocomputing 2006, 69:2385-2389.
14. Wang F., Zhang Changshui Robust self-tuning semi-supervised learning. Neurocomputing 2007, 70:2931-2939.
15. Semi-supervised Learning 2006, MIT Press, Cambridge, MA. O. Chapelle, B. Schölkopf, A. Zien (Eds.).
16. X. Zhu, Semi-supervised Learning Literature Survey, Computer Sciences Technical Report 1530, University of Wisconsin-Madison, 2008.
17. D. Yarowsky, Unsupervised word sense disambiguation rivaling supervised methods, in: Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, 1995, pp. 189-196.
18. E. Riloff, J. Wiebe, T. Wilson, Learning subjective nouns using extraction pattern bootstrapping, in: Proceedings of the Seventh Conference on Natural Language Learning, 2003.
19. Adankon M.M., Cheriet M. Help-training for semi-supervised support vector machines. Pattern Recognition 2011, 44(9):2220-2230.
20. C.S. Oliveira, F.G. Cozman, I. Cohen, Splitting the unsupervised and supervised components of semi-supervised learning, in: Proceedings of the 22nd ICML Workshop on Learning with Partially Classified Training Data, Bonn, Germany, 2005.
21. Ando R., Zhang T. A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res. 2005, 6:1817-1853.
22. Jain A.K., Murty M.N., Flynn P.J. Data clustering. a review. ACM Comput. Surveys 1999, 31(3):264-323.
23. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithm 1981, Plenum Press, New York.
24. S. Basu, A. Banerjee, R.J. Rooney, Semi-supervised clustering by seeding, in: Proceedings of the 19th International Conference on Machine Learning, 2002, pp. 19-26.
25. A. Bar-Hillel, T. Hertz, N. Shental, D. Weinshall, Learning distance functions using equivalence relations, in: Proceedings of the 20th International Conference on Machine Learning, 2003, pp. 11-18.
26. H. Gan, X. Tong, Q. Jiang, N. Sang, X. Kong, F. Wang, Discussion of FCM algorithm with partial supervision, in: Proceedings of the Eighth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 2009, pp. 27-31.
27. X. Tong, Q. Jiang, N. Sang, H. Gan, S. Zeng, The feature weighted FCM algorithm with semi-supervised, in: Proceedings of the Eighth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 2009, pp. 22-26.
28. Vapnik V.N. Statistical Learning Theory 1998, John Wiley and Sons, New York.
29. Shawe-Taylor J., Sun S. A review of optimization methodologies in support vector machines. Neurocomputing 2011, 74:3609-3618.
30. C. Chang, C. Lin, LIBSVM: A Library for Support Vector Machines, Available at, 2011. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
31. C.C. Blake, C.J. Merz, UCI Repository of Machine Learning Databases, 2010. http://www.ics.uci.edu/~mlearn/MLRepository.html.
32. Chapelle O., Sindhwani V., Keerthi S.S. Optimization techniques for semi-supervised support vector machines. J. Mach. Learn. Res. 2008, 9:203-233.
33. T. Joachims, Transductive inference for text classification using support vector machines, in: Proceedings of the 16th International Conference on Machine Learning, 1999, pp. 200-209.
34. Collobert R., Sinz F., Weston J., Bottou L. Large scale transductive SVMs. J. Mach. Learn. Res. 2006, 7:1687-1712.
35. Y. Li, Z. Zhou, Towards making unlabeled data never hurt, in: Proceedings of the 28th International Conference on Machine Learning, 2011.