Evaluating feature selection methods for learning in data mining applications

European Journal of Operational Research

Volumn 156, Issue 2, 2004, Pages 483-494

  Piramuthu, Selwyn ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

Artificial intelligence; Credit risk analysis; Decision trees; Feature selection

Indexed keywords

COMPUTATIONAL COMPLEXITY; COSTS; DATA MINING; DECISION THEORY; FEATURE EXTRACTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL PROGRAMMING; OPERATIONS RESEARCH; PROBABILISTIC LOGICS; RISK ASSESSMENT; TREES (MATHEMATICS);

CREDIT RISK ANALYSIS; DATA PREPROCESSING; DECISION TREES;

ARTIFICIAL INTELLIGENCE;

EID: 1442307850     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-2217(02)00911-6     Document Type: Article

References (39)

1. Abdel-Khalik A.R., El-Sheshai K.M. Information choice and utilization in an experiment on default prediction. Journal of Accounting Research, Autumn. 1980;325-342.
2. Almuallim, H.M., Dietterich, T.G., 1991. Learning with many irrelevant features. In: Proceedings of the Ninth National Conference on Artificial Intelligence. pp. 547-552.
3. Battiti R. Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks. 5(4):1994;537-550.
4. Bradley P.S., Mangasarian O.L., Street W.N. Feature selection in mathematical programming. INFORMS Journal on Computing. 10(2):1998;209-217.
5. Cover T.M. The best two independent measurements are not the two best. IEEE Transactions on Systems, Man, and Cybernetics. (January):1974;116-117.
6. Devijver P.A., Kittler J. Pattern Recognition: A Statistical Approach. 1982;Prentice-Hall, Englewood Cliffs, NJ.
7. Elashoff J.D., Elashoff R.M., Goldman G.E. On the choice of variables in classification problems with dichotomous variables. Biometrika. 54:1967;668-670.
8. Elomaa, T., Ukkonen, E., 1994. A geometric approach to feature selection. In: Proceedings of the European Conference on Machine Learning. pp. 351-354.
9. Fukunaga K. Introduction to Statistical Pattern Recognition. 1972;Academic Press, New York.
10. Hopkins, C., Routen, T., Watson, T., 1994. Problems with using genetic algorithms for neural network feature selection. 11th European Conference on Artificial Intelligence. pp. 221-225.
11. John, G.H., Kohavi, R., Pfleger, K., 1994. Irrelevant features and the subset selection problem. In: Proceedings of the Eleventh International Conference on Machine Learning. pp. 121-129.
12. Kerber R., Livezy B., Simoudis E. Goonatilake S., Khebbal S. A Hybrid System for Data Mining. Intelligent Hybrid Systems. 1995;John Wiley, New York.
13. Kira, K., Rendell, L.A., 1992. A practical approach to feature selection. In: Proceedings of the Ninth International Conference on Machine Learning. pp. 249-256.
14. Kittler J. Mathematical methods of feature selection in pattern recognition. International Journal of Man-Machine Studies. 7:1975;609-637.
15. Kohavi, R., Frasca, B., 1994. Useful feature subsets and rough sets reducts. Third International Workshop on Rough Sets and Soft Computing (RSSC 94).
16. Kohavi, R., 1995. Wrappers for Performance Enhancement and Oblivious Decision Graphs, Ph.D. Dissertation, Computer Science Department, Stanford University.
17. Koller, D., Sahami, M., 1996. Toward optimal feature selection. In: Machine Learning: Proceedings of the Thirteenth International Conference.
18. Kononenko, I., 1994. Estimating attributes: Analysis and extensions of RELIEF. In: Proceedings of the European Conference on Machine Learning. pp. 171-182.
19. Krivda C.D. Data-mining dynamite. BYTE. (October):1995;97-102.
20. Liu H., Motoda H. Feature Extraction, Construction, and Selection: A Data Mining Perspective. 1998;Kluwer Academic Publishers, Dordrecht.
21. Liu H., Motoda H. Feature Selection for Knowledge Discovery and Data Mining. 1998;Kluwer Academic Publishers, Dordrecht.
22. Modrzejewski, M., 1993. Feature selection using rough sets theory, In: European Conference on Machine Learning. pp. 213-226.
23. Malki H.A., Moghaddamjoo A. Using the Karhunen-Loe've transformation in the back-propagation training algorithm. IEEE Transactions on Neural Networks. 2(1):1991;162-165.
24. Meisel W.S. Computer-Oriented Approaches to Pattern Recognition. 1972;Academic Press, New York.
25. Milne, L., 1995. Feature Selection using Neural Networks with Contribution Measures, AI'95 (November), Canberra.
26. O'Reilly, S., 1995. Why many mining tools cannot dig deep enough. Computer Weekly, 2 (February), 24.
27. Pawlak Z. Rough sets. International Journal of Computer and Information Sciences. 11(5):1982;341-356.
28. Piramuthu, S., Shaw, M.J., 1994. On using decision tree as feature selector for feed-forward neural networks, In: International Symposium on Integrating Knowledge and Neural Heuristics. pp. 67-74.
29. Pudil, P., Ferri, F.J., Novovicova, J., Kittler, J., 1994. Floating search methods for feature selection with nonmonotonic criterion functions. IEEE 12th International Conference on Pattern Recognition. Vol. II, 279-283.
30. Quinlan J.R. Simplifying decision trees. International Journal of Man-Machine Studies. 27:1987;221-234.
31. Quinlan J.R. Decision trees and decision making. IEEE Transactions on Systems, Man and Cybernetics. 20(2):1990;339-346.
32. Seshadri, V., Weiss, S.M., Sasisekharan, R., 1995. Feature extraction for massive data mining. In: Proceedings of First International Conference on Knowledge Discovery and Data Mining. pp. 258-262.
33. Setino R., Liu H. Neural network feature selector. IEEE Transactions on Neural Networks. 8(3):1997;654-662.
34. Siedlecki W., Sklansky J. A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters. 10(5):1989;335-347.
35. Stearns, S.D., 1976. On selecting features for pattern classifiers, Third International Conference on Pattern Recognition. pp. 71-75.
36. Tam K.Y., Kiang M.Y. Managerial applications of neural networks: The case of bank failure predictions. Management Science. 38(7):1992;926-947.
37. Toussaint G.T. Note on optimal selection of independent binary-valued features for pattern recognition. IEEE Transactions on Information Theory IT. 17:1971;618.
38. Whitebread K.R., Jameson S. Information discovery in high-volume, frequently changing data. IEEE Expert. (October):1995;51-53.
39. Yang, J., Honavar, V., 1997. Feature subset selection using a Genetic Algorithm. In: Proceedings of the Genetic Programming Conference (GP'97). pp. 380-385.