Fuzzy mutual information based min-redundancy and max-relevance heterogeneous feature selection

International Journal of Computational Intelligence Systems

Volumn 4, Issue 4, 2011, Pages 619-633

  Yu, Daren ^a   An, Shuang ^a   Hu, Qinghua ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Feature selection; Fuzzy mutual information; Redundancy; Relevance; Stability

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); CONVERGENCE OF NUMERICAL METHODS; LEARNING SYSTEMS; REDUNDANCY;

BENCHMARK DATA; DIFFERENTIAL ENTROPY; FUZZY FEATURES; FUZZY INFORMATION; HETEROGENEOUS FEATURES; MUTUAL INFORMATIONS; PRE-PROCESSING STEP; RELEVANCE;

FEATURE EXTRACTION;

EID: 80052244766     PISSN: 18756891     EISSN: 18756883     Source Type: Journal    
DOI: 10.1080/18756891.2011.9727817     Document Type: Article

References (47)

1. Battiti, R., 1994. Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5:531–549.
2. Ching, J. Y., Wong, A. K. C., and Chan, K. C. C., 1995. Classdependent discretization for inductive learning form continuous and mixed-mode data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17:641–651.
3. Blake, C. L., and Merz, C. J., 1998. UCI Repository of Machine Learning Databases http://www.ics.uci.edu/mlearn/MLRepository.html
4. Corts, C., and Vapnik, V., 1995. Support vector networks. Machine Learning, 20:1–25.
5. Cover, T. M., 1974. The best two independent measurements are not the two best. IEEE Transactions on Systems, Man, and Cybernetics, 4:116–117.
6. Dash, M., and Liu, H., 2003. Consistency-based search in feature selection. Artificial Intelligence, 151:155–176.
7. David, A. B., and Wang, H., 2000. A formalism for relevance and its application in feature subset selection. Machine Learning, 41:175–195.
8. Ding, C., and Peng, H. C., Minimum redundancy feature selection from microarray gene expression data. Proceeding of the 2003 IEEE Computational Systems Bioinformatics Conference. Stanford, California. pp. 523–528.
9. Duda, R., Hart, P., and Stork, D., 2001. Pattern classification and scene analysis, New York:Wiley.
10. Farmer, M. E., Bapna, S., and Jain, A. K., Large scale feature selection using modified random mutation hill climbing. Proceedings of the 17th International Conference on Pattern Recognition. UK. Vol. 2, pp. 287–290.
11. Hall, M. A., Correlation-based feature selection for discrete and numeric class machine learning. Proceedings of the Seventeenth International Conference on Machine Learning. pp. 359–366. New Zealand:Hamilton.
12. Hu, Q. H., Xie, Z. X., and Yu, D. R., 2007. Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recognition, 40:3509–3521.
13. Hu, Q. H., Yu, D. R., and Xie, Z. X., 2006. Informationpreserving hybrid data reduction based on fuzzyrough techniques. Pattern Recognition Letters, 27:414–423.
14. Hu, Q. H., Liu, J. F., and Yu, D. R., 2008. Stability Analysis on Rough Set Based Feature Evaluation, Vol. 5009, 88–96. Berlin/Heidelberg:Springer. Lecture Notes in Computer Science
15. Kalousis, A., Prados, J., and Hilario, M., 2007. Stability of feature selection algorithms:a study on highdimensional spaces. Knowledge and Information Systems, 12:95–116.
16. Ke, L. J., Feng, Z. R., and Ren, Z. G., 2008. An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recognition Letters, 29:1351–1357.
17. Kononenko, I., 1994. Estimating Attributes:Analysis and Extensions of RELIEF. European Conference on Machine Learning,:171–182.
18. Kwak, N., and Choi, C.-H., 1994. Input Feature selection by mutual information based on Parzen window. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:1667–1671.
19. Kwak, N., and Choi, C.-H., 2002. Input feature selection for classification problems. IEEE Transaction on Neural Networks, 13:143–159.
20. Lee, H.-M., M Chen, C.-, Chen, J.-M., and Jou, Y.-L., 1999. An efficient fuzzy classifier with feature selection based on fuzzy information entropy. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, 31:426–432.
21. Leo, B., Jerome, H. F., Richard, A. O., and Charls, J. S., 1993. Classification and regression trees, New York:Chapman and Hall.
22. Li, Y. H., Dong, M., and Hua, J., 2008. Localized feature selection for clustering. Pattern Recognition Letters, 29:10–18.
23. Liu, H., and Motoda, H., 1998. Feature selection for knowledge discovery and data mining, Boston:Kluwer Academic Publishers.
24. Liu, X. X., Krishnan, A., and Mondry, A., 2005. An entropybased gene selection method for cancer classification using microarray data. BMC Bioinformatics, 6:1–14.
25. Maritz, J. S., 1981. Distribution-free statistical methods, Vol. 217, Chapman Hall.
26. Narendra, P., and Fukunaga, K., 1997. A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers, 26:917–922.
27. Patrick, E. A., and Fisher, F. P., 1970. A generalized k-nearest neighbor rule. Information and Control, 16:128–152.
28. Pawlak, Z., and Rauszer, C., 1985. Dependency of attributes in information systems. Bull. Polish Acad. Sci. Math., 33:551–559.
29. Peng, H. C., Long, F. H., and Ding, C., 2005. Feature selection based on mutual information:criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27:1226–1238.
30. Prasad, M., 2008. Online feature selection for classifying emphysema in HRCT images. International Journal of Computational Intelligence Systems, 1:127–133.
31. Pudil, P., Novovicova, J., and Kittler, J., 1994. Floating search methods in feature selection. Pattern Recognition Letters, 15:1119–1125.
32. Qian, Y. H., Liang, J. Y., and Dang, C. G., 2008. Consistency measure, inclusion degree and fuzzy measure in decision tables. Fuzzy Sets and Systems, 159:2353–2377.
33. Qian, Y. H., Liang, J. Y., Dang, C. G., Zhang, H. Y., and Ma, J. M., 2008. On the evaluation of the decision performance of an incomplete decision table. Data and Knowledge Engineering, 65:373–400.
34. Schlimmer, J. C., Efficiently inducing determinations:a complete and systematic search algorithm that uses optimal pruning. Proceedings of Tenth International Conference on Machine Learning. pp. 284–290. MA:Morgan Kaufmann.
35. Shannon, C. E., 1948. A mathematical theory of communication. The Bell System Technical Journal, 27:379–423.
36. Shen, Q., and Jensen, R., 2004. Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring. Pattern Recognition, 37:1351–1363.
37. Sima, C., and Dougherty, E. R., 2008. The peaking phenomenon in the presence of feature-selection. Pattern Recognition Letters, 29:1667–1674.
38. Somol, P., Pudil, P., Novoviova, J., and Paclik, P., 1999. Adaptive floating search methods in feature selection. Pattern Recognition Letters, 20:1157–1163.
39. Somol, P., Pudil, P., and Kittler, J., 2004. Fast branch and bound algorithms for optimal feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26:900–912.
40. Suarez, M. R., Vilar, J. R., and Grande, J., 2007. “ A feature selection method using a fuzzy mutual information measure ”. In Advances in Soft Computing, Vol. 44, 56–63. Berlin / Heidelberg:Springer.
41. Swiniarski, R. W., and Skowron, A., 2003. Rough set methods in feature selection and recognition. Pattern Recognition Letters, 24:833–849.
42. Tang, W. Y., and Mao, K. Z., 2007. Feature selection algorithm for mixed data with both nominal and continuous features. Pattern Recognition Letters, 28:563–571.
43. Wang, Q., Shen, Y., and Zhang, Y., 2003. A quantitative method for evaluating the performances of hyperspectral image fusion. IEEE transactions on Instrumentation and Measurement, 52:1041–1047.
44. Wang, Q., Shen, Y., and Zhang, J. Q., 2005. Nonlinear correlation measure for multivariable data set. PhysicaDNonlinear Phenomena, 200:287–295.
45. Wei, Z. H., Miao, D. Q., Chauchat, J.-H., and Wen, R. Z., 2009. LiN-grams based feature selection and text representation for Chinese Text Classification. International Journal of Computational Intelligence Systems, 2:365–374.
46. Wu, W. Z., 2008. Attribute reduction based on evidence theory in incomplete decision systems. Information Sciences, 178:1355–1371.
47. Yu, L., and Liu, H., 2004. Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5:1205–1224.