Exploiting unlabeled data for improving accuracy of predictive data mining

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2003, Pages 267-274

  Peng, Kang ^a   Vucetic, Slobodan ^a   Han, Bo ^a   Xie, Hongbo ^a   Obradovic, Zoran ^a  

^a TEMPLE UNIVERSITY   (United States)

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EID: 78149327484     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. S.F. Altschul, T.L. Madden, A.A. Schiffer, J. Zhang, Z. Zhang, W. Miller and D.J. Lipman, "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res., 1997, vol. 25, pp. 3389-3402.
2. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training", In Proc. of COLT'98, 1998, pp. 92-100.
3. B. Boeckmann, A. Bairoch, R. Apweiler, M. C. Blatter, A. Estreicher, E. Gasteiger, M. J. Martin, K. Michoud, C. O'Donovan, I. Phan, S. Pilbout and M. Schneider, "The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003", Nucleic Acids Res., 2003, vol. 31, pp. 365-370.
4. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees, Wadsworth Inc., 1984, pp. 43-49.
5. L. Breiman, "Bias, variance, and arcing classifiers", Technical Report 460, UC-Berkeley, 1996.
6. A. P. Dempster, N. M. Laird and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm", J. Roy. Statistical Society (B), 1977, vol. 39, pp. 1-38.
7. A. K. Dunker, C. J. Brown, J. D. Lawson, L. M. Iakoucheva and Z. Obradovic, "Intrinsic disorder and protein function", Biochemistry, 2002, vol. 41(21), pp. 6573-6582.
8. N. Japkowicz, "The class imbalance problem: significance and strategies", In Proc. of IC-AI'00, 2000.
9. B. Liu, W. S. Lee, P. S. Yu and X. Li, "Partially supervised classification of text documents", In Proc. of ICML'02, 2002, pp. 387-394.
10. K. Nigam, A. McCallum, S. Thrun and T. Mitchell, "Text classification from labeled and unlabeled documents using EM", Mach. Learning, 2000, vol. 39(2/3), pp. 103-134.
11. Z. Obradovic, K. Peng, S. Vucetic, P. Radivojac, C. J. Brown and A. K. Dunker, "Predicting intrinsic disorder from amino acid sequence", Proteins, Special Issue on CASP5, in press.
12. J. C. Platt, "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods", In Advances in Large Margin Classifiers, A. J. Smola, P. Bartlett, B. Scholkopf, D. Schuurmans (eds.): MIT Press, 1999, pp. 61-74.
13. M. F. Porter, "An algorithm for suffix stripping", Program, 1980, vol. 14(3), pp. 130-137.
14. F. Provost and P. Domingos, "Well-trained PETs: Improving probability estimation trees", CeDER Working Paper #IS-0004, Stern School of Business, New York University, 2000.
15. M. D. Richard and R. P. Lippmann, "Neural network classifiers estimate Bayesian a posteriori probabilities", Neural Comput., 1991, vol. 3, pp. 461-483.
16. G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval", Inf. Process. Manage., 1988, vol. 24(5), pp. 513-523.
17. M. Seeger, "Learning with labeled and unlabeled data", Technical Report, Institute for Adaptive and Neural Computation, University of Edinburgh, 2001.
18. B. Shahshahani and D. Landgrebe, "The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon", IEEE Trans. Geosci. Remote Sens., 1994, vol. 32(5), pp. 1087-1095.
19. D. M. J. Tax and R. P. W. Duin, "Uniform Object Generation for Optimizing One-class Classifiers", J. Mach. Learn. Res., Special Issue on Kernel Methods, 2002, vol. 2(2), pp. 155-173.
20. V. N. Vapnik, Statistical Learning Theory, Wiley, 1998.
21. S. Vucetic, D. Pokrajac, H. Xie and Z. Obradovic, "Detection of underrepresented biological sequences using class-conditional distribution models", In Proc. of SIAM SDM'03, 2003, pp. 279-283.
22. Y. Wu, Q. Tian and T. S. Huang, "Integrating unlabeled images for image retrieval based on relevance feedback", In Proc. of ICPR'00, 2000.
23. G. Yona, N. Linial and M. Linial, "ProtoMap: Automatic classification of protein sequences, a hierarchy of protein families, and local maps of the protein space", Proteins, 1999, vol. 37, pp. 360-378.
24. B. Zadrozny and C. Elkan, "Transforming classifier scores into accurate multiclass probability estimates", In Proc. of KDD'02, 2002, pp. 694-699.