Feature weighting by RELIEF based on local hyperplane approximation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7301 LNAI, Issue PART 2, 2012, Pages 335-346

  Cai, Hongmin ^a   Ng, Michael ^b  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Classification; Feature weighting; KNN; local hyperplane; RELIEF

Indexed keywords

EMPIRICAL STUDIES; FEATURE WEIGHT; FEATURE WEIGHTING; GLOBAL MEASUREMENTS; HYPER-PLANE APPROXIMATION; KNN; LOCAL APPROXIMATION; LOCAL HYPERPLANE; REAL WORLD DATA; RELIEF;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA MINING; DEGRADATION; VIRTUAL REALITY;

GEOMETRY;

EID: 84861444374     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-30220-6_28     Document Type: Conference Paper

References (31)

1. Asuncion, A., Newman, D.: UCI machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
2. Bachrach, G.R., Navot, A., Tishby, N.: Margin Based Feature Selection - Theory and Algorithms. In: Proc. 21st International Conference on Machine Learning (ICML), pp. 43-50 (2004)
3. Brown, G.: An Information Theoretic Perspective on Multiple Classifier Systems. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 344-353. Springer, Heidelberg (2009)
4. Brown, G.: Some Thoughts at the Interface of Ensemble Methods and Feature Selection. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 314-314. Springer, Heidelberg (2010)
5. Cawley, G.C., Talbot, N.L.C., Girolami, M.: Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation. Advances in Neural Information Processing Systems 19 (2007)
6. Christopher, A., Andrew, M., Stefan, S.: Locally weighted learning. Artificial Intelligence Review 11, 11-73 (1997)
7. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3(2), 185-205 (2005)
8. Domeniconi, C., Peng, J., Gunopulos, D.: Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9), 1281-1285 (2002)
9. Duan, K.B.B., Rajapakse, J.C., Wang, H., Azuaje, F.: Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Transactions on Nanobioscience 4(3), 228-234 (2005)
10. Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley (2001)
11. Fraley, C., Raftery, A.E.: Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97(458), 611-631 (2002)
12. Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. BMC bioinformatics 16, 906-914 (2000)
13. Girolami, M., He, C.: Probability density estimation from optimally condensed data samples. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1253-1264 (2003)
14. Guyon, I.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157-1182 (2003)
15. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389-422 (2002)
16. Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 607-616 (1996)
17. Huang, C.J., Yang, D.X., Chuang, Y.T.: Application of wrapper approach and composite classifier to the stock trend prediction. Expert Systems with Applications 34(4), 2870-2878 (2008)
18. Koller, D., Sahami, M.: Toward optimal feature selection. In: Saitta, L. (ed.) Proceedings of the Thirteenth International Conference on Machine Learning (ICML), pp. 284-292. Morgan Kaufmann Publishers (1996)
19. Kononenko, I.: Estimating Attributes: Analysis and Extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171-182. Springer, Heidelberg (1994)
20. Kwak, N., Choi, C.H.: Input feature selection by mutual information based on parzen window. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1667-1671 (2002)
21. Liu, H., Setiono, R.: Feature selection via discretization. IEEE Transactions on Knowledge and Data Engineering 9, 642-645 (1997)
22. Narlikar, L., Hartemink, A.J.: Sequence features of dna binding sites reveal structural class of associated transcription factor. Bioinformatics 22(2), 157-163 (2006)
23. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer (August 2000)
24. Peng, Y.H.: A novel ensemble machine learning for robust microarray data classification. Computers in Biology and Medicine 36, 553-573 (2006)
25. Shakhnarovich, G., Darrell, T., Indyk, P. (eds.): Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. MIT Press (2006)
26. Statnikov, A., Wang, L., Aliferis, C.F.: A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 9, 319-328 (2008)
27. Sun, Y.: Iterative relief for feature weighting: Algorithms, theories, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1035-1051 (2007)
28. Sun, Y., Todorovic, S., Goodison, S.: Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1610-1626 (2010)
29. Tao, Y., Vojislav, K.: Adaptive local hyperplane classification. Neurocomputing 71(13-15), 3001-3004 (2008)
30. Vincent, P., Bengio, Y.: K-local hyperplane and convex distance nearest neighbor algorithms. In: Advances in Neural Information Processing Systems, pp. 985-992. The MIT Press (2001)
31. Zhang, T., Oles, F.J.: Text categorization based on regularized linear classification methods. Information Retrieval 4(1), 5-31 (2001)