Sharpened graph ensemble for semi-supervised learning

Intelligent Data Analysis

Volumn 17, Issue 3, 2013, Pages 387-398

  Choi, Inae ^a   Park, Kanghee ^a   Shin, Hyunjung ^a  

^a Ajou University   (South Korea)

Author keywords

Data mining; ensemble learning; graph sharpening; machine learning; noise reduction; parameter selection; semi supervised learning

Indexed keywords

ENSEMBLE LEARNING; GENERALIZATION ABILITY; GRAPH SHARPENING; PARAMETER SELECTION; PERFORMANCE OF ALGORITHM; REAL-WORLD PROBLEM; SEMI-SUPERVISED LEARNING; TECHNICAL DIFFICULTIES;

DATA MINING; LEARNING ALGORITHMS; LEARNING SYSTEMS; NOISE ABATEMENT;

SUPERVISED LEARNING;

EID: 84878897014     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-130585     Document Type: Article

References (38)

1. O. Chapelle et al., Cluster kernels for semi-supervised learning, Advances in Neural Information Processing Systems 15 (2003), 585-592.
2. X. Zhu, Semi-supervised learning with graphs, PA 15213, Carnegie Mellon, Pittsburgh, 2005.
3. O. Chapelle et al., Semi-Supervised Learning, Cambridge, England: MIT Press, 2006.
4. J. Wang et al., On efficient large margin semisupervised learning: Method and theory, Journal of Machine Learning Research 10 (2009), 719-742.
5. X. Zhu, Semi-supervised learning literature survey, Technical Report TR-1530, Wisconsin-Madson, 2008.
6. H. Shin and K. Tsuda, Prediction of protein function from networks, in: Semi-Supervised Learning, O. Chapelle et al., eds, ed: MIT press, 2006, pp. 339-352.
7. H. Shin et al., Graph sharpening plus graph integration: A synergy that improves protein functional classification, Bioinformatics 23 (2007), 3217-3224.
8. A. Subramanya and J. Bilmes, Soft-supervised learning for text classification, in: Conference on Empirical Methods in Natural Language Processing, Honolulu, Hawaii, (2008), 1090-1099.
9. R.K. Ando and T. Zhang, A high-performance semi-supervised learning method for text chunking, in: Annual Meeting on Association for Computational Linguistics, Ann Arbor, Michigan, (2005), 1-9.
10. L. Wei and E. Keogh, Semi-supervised time series classification, in: International Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, (2006), 748-753.
11. E. Bair and R. Tibshirani, Semi-supervised methods to predict patient survival from gene expression data, PLoS Biology 2 (2004), 511-522.
12. Y.-C. Gong and C.-L. Chen, Semi-supervised method for gene expression data Classification cation with gaussian fields and harmonic functions, in: International Conference on Pattern Recognition, Tampa, FL, (2008), 1-4.
13. N. Morsillo et al., Semi-supervised learning of visual classifiers from web images and text, in: International Joint Conference on Artificial Intelligence, California, USA, (2009), 1169-1174.
14. A. Celikyilmaz et al., A graph-based semi-supervised learning for question-answering, in: Annual Meeting of the Association for Computational Linguistics, Singapore, (2009), 719-727.
15. R. Liu et al., A graph-based semi-supervised learning algorithm for web page classification, in: International Conference on Intelligent Systems Design and Applications, China, (2006), 856-860.
16. Y. Bengio, Gradient-based optimization of hyperparameters, Neural Computation 12 (2000), 1889-1900.
17. L. Breiman, Bagging predictors, Machine Learning 24 (1996) 123-140.
18. M.P. Perrone, Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization, PhD Thesis, Brown University, Providence, RI 1993.
19. A.J.C. Sharkey and N.E. Sharkey, Combining diverse neural nets, The Knowledge Engineering Review 12 (1997), 231-247.
20. K. Tumer and J. Ghosh, Error correlation and error reduction in ensemble classifiers, Connection Science 8 (1996), 385-404.
21. H. Shin et al., Graph sharpening, Expert Systems with Applications, 2010.
22. H. Shin et al., Graph sharpening, Expert Systems with Applications 37 (2010), 7870-7879.
23. J. He et al., Graph-based semi-supervised learning as a generative model, in: International Joint Conference on Artificial Intelligence, Hyderabad, India, (2007), 2492-2497.
24. D. Zhou et al., Learning with local and global consistency, Advances in Neural Information Processing Systems 16 (2004), 321-328.
25. Y. Song et al., Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images, Pattern Analysis and Applications 12 (2009), 99-115.
26. M. Belkin et al., Regression and regularization on large, in: COLT 2004 LNCS (LNAI), J. Shawe-Taylor and Y. Singer, eds, 3120 (2003), 624-638.
27. D.A. Spielman and S.-H. Teng, Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time, Journal of the ACM 51 (2004), 385-463.
28. Y. Freund and R.E. Schapire, Experiments with a new boosting algorithm, Machine Learning: Proceedings of the Thirteenth International Conference (1996), 148-156.
29. H. Shin and S. Cho, Pattern selection using the bias and variance of ensemble, Journal of the Korean Institute of Industrial Engineers 28 (2001), 112-127.
30. Y. Freund and R.E. Schapire, Experiments with a new boosting algorithm, in: Proceedings 13th International Conference on Machine Learning (1996), 148-156.
31. T.K. Ho, The random subspace method for constructing decision forests, IEEE Trans Pattern Analysis and Machine Intelligence 20 (1998), 832-844.
32. M. Skurichina and R.P.W. Duin, Bagging, boosting and the random subspace method for linear classifiers, Pattern Analysis and Applications 5 (2002), 121-135.
33. M. Vuk and T. Curk, ROC curve, lift chart and calibration plot, Metodoloski Zvezki 3 (2006), 89-108.
34. J. Huang and C.X. Ling, Using AUC and accuracy in evaluating learning algorithms, IEEE Educational Activities Department 17 (2005), 299-310.
35. T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters 27 (2006), 861-874.
36. J. Demsar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research 7 (2006), 1-30.
37. H. Shin and K. Tsuda, Prediction of protein function from networks, in: Semi-Supervised Learning, O. Chapelle et al., eds, ed Cambridge, Massachusetts, London, England: The MIT Press, 2006, pp. 361-376.
38. K. Tsuda et al., Fast protein classification with multiple networks, Bioinformatics 21 (2005), 59-65.