Irrelevant Features and the Subset Selection Problem

Proceedings of the 11th International Conference on Machine Learning, ICML 1994

Volumn , Issue , 1994, Pages 121-129

  John, George H ^a   Kohavi, Ron ^a   Pfleger, Karl ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SET THEORY;

CROSS VALIDATION; DEGREE OF RELEVANCE; FEATURE SUBSET SELECTION; HIGH-ACCURACY; INDUCTION ALGORITHMS; MACHINE LEARNING LITERATURE; SELECTION ALGORITHM; SELECTION PROBLEMS; SUBSET SELECTION; TARGET CONCEPT;

FEATURE SELECTION;

EID: 85099325734     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/B978-1-55860-335-6.50023-4     Document Type: Conference Paper

References (44)

1. Almuallim, H., and Dietterich, T. G. 1991. Learning with many irrelevant features. In Ninth National Conference on Artificial Intelligence, 547-552. MIT Press.
2. Ben-Bassat, M. 1982. Use of distance measures, information measures and error bounds in feature evaluation. In Krishnaiah, P. R., and Kanal, L. N., eds., Handbook of Statistics, volume 2. North-Holland Publishing Company. 773-791.
3. Blum, A. L., and Rivest, R. L. 1992. Training a 3-node neural network is NP-complete. Neural Networks 5:117-127.
4. Blumer, A.; Ehrenfeucht, A.; Haussler, D.; and Warmuth, M. K. 1987. Occam's razor. Information Processing Letters 24:377-380.
5. Boyce, D.; Farhi, A.; and Weischedel, R. 1974. Optimal Subset Selection. Springer-Verlag.
6. Breiman, L.; Friedman, J. H.; Olshen, R. A.; and Stone, C. J. 1984. Classification and Regression Trees. Wadsworth International Group.
7. Cardie, C. 1993. Using decision trees to improve case-based learning. In Proceedings of the Tenth International Conference on Machine Learning, 25-32. Morgan Kaufmann.
8. Caruana, R., and Freitag, D. 1994. Greedy attribute selection. In Cohen, W. W., and Hirsh, H., eds., Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann.
9. Cohen, W. W. 1993. Efficient pruning methods for separate-and-conquer rule learning systems. In 13th International Joint Conference on Artificial Intelligence, 988-994. Morgan Kaufmann.
10. Devijver, P. A., and Kittler, J. 1982. Pattern Recognition: A Statistical Approach. Prentice-Hall International.
11. Draper, N. R., and Smith, H. 1981. Applied Regression Analysis. John Wiley & Sons, 2nd edition.
12. Gennari, J. H.; Langley, P.; and Fisher, D. 1989. Models of incremental concept formation. Artificial Intelligence 40:11-61.
13. H a n c o c k, T . R. 1989. O n t h e difficulty of finding s m a l l c o n s i s t e n t decision t r e e s. U n p u b l i s h e d M a n u s c r i p t, H a r v a r d University.
14. Holte, R. C. 1993. Very simple classification rules perform well on most commonly used datasets. Machine Learning 11:63-90.
15. Kira, K., and Rendell, L. A. 1992a. The feature selection problem: Traditional methods and a new algorithm. In Tenth National Conference on Artificial Intelligence, 129-134. MIT Press.
16. Kira, K., and Rendell, L. A. 1992b. A practical approach to feature selection. In Proceedings of the Ninth International Conference on Machine Learning. Morgan Kaufmann.
17. Kononenko, I. 1994. Estimating attributes: Analysis and extensions of Relief. In Proceedings of the European Conference on Machine Learning.
18. Langley, P., and Sage, S. 1994. Oblivious decision trees and abstract cases. In Working Notes of the AAAI94 Workshop on Case-Based Reasoning. In press.
19. Levy, A. Y. 1993. Irrelevance Reasoning in Knowledge Based Systems. Ph.D. Dissertation, Stanford University.
20. Littlestone, N. 1988. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning 2:285-318.
21. p. Technometrics 15:661-675.
22. Marill, T., and Green, D. M. 1963. On the effectiveness of receptors in recognition systems. IEEE Transactions on Information Theory 9:11-17.
23. Miller, A. J. 1990. Subset Selection in Regression. Chapman and Hall.
24. Modrzejewski, M. 1993. Feature selection using rough sets theory. In Brazdil, P. B., ed., Proceedings of the European Conference on Machine Learning, 213-226.
25. Moore, A. W., and Lee, M. S. 1994. Efficient algorithms for minimizing cross validation error. In Cohen, W. W., and Hirsh, H, eds., Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann.
26. Moret, B. M. E. 1982. Decision trees and diagrams. ACM Computing Surveys 14(4):593-623.
27. Mucciardi, A. N., and Gose, E. E. 1971. A comparison of seven techniques for choosing subsets of pattern recognition properties. IEEE Transactions on Computers C-20(9):1023-1031.
28. Murphy, P. M., and Aha, D. W. 1994. UCI repository of machine learning databases. For information contact ml-repository@ics.uci .edu.
29. Narendra, M. P., and Fukunaga, K. 1977. A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers C-26(9):917-922.
30. Neter, J.; Wasserman, W.; and Kutner, M. H. 1990. Applied Linear Statistical Models. Irwin: Homewood, IL, 3rd edition.
31. Pagallo, G., and Haussler, D. 1990. Boolean feature discovery in empirical learning. Machine Learning 5:71-99.
32. Quinlan, J. R. 1986. Induction of decision trees. Machine Learning 1:81-106. Reprinted in Shavlik and Dietterich (eds.) Readings in Machine Learning.
33. Quinlan, J. R. 1992. CJ^.5: Programs for Machine Learning. Los Altos, California: Morgan Kaufmann.
34. Rissanen, J. 1986. Stochastic complexity and modeling. Ann. Statist 14:1080-1100.
35. Russel, S. J. 1986. Preliminary steps toward the automation of induction. In Proceedings of the National Conference on Artificial Intelligence, 477-484.
36. Russel, S. J. 1989. The Use of Knowledge in Analogy and Induction. Morgan Kaufmann.
37. Schlimmer, J. C. 1993. Efficiently inducing determinations: A complete and systematic search algorithm that uses optimal pruning. In Proceedings of the Tenth International Conference on Machine Learning, 284-290. Morgan Kaufmann.
38. Siedlecki, W., and Sklansky, J. 1988. On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence 2(2): 197-220.
39. Skalak, D. B. 1994. Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Cohen, W. W., and Hirsh, H, eds., Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann.
40. Thrun, S. B., et al. 1991. The monk's problems: A performance comparison of different learning algorithms. Technical Report CMU-CS-91-197, Carnegie Mellon University.
41. Vafai, H, and De Jong, K. 1992. Genetic algorithms as a tool for feature selection in machine learning. In Fourth International Conference on Tools with Artificial Intelligence, 200-203. IEEE Computer Society Press.
42. Wallace, C, and Freeman, P. 1987. Estimation and inference by compact coding. Journal of the Royal Statistical Society (B) 49:240-265.
43. Weiss, S. M., a n d Kulikowski, C. A. 1 9 9 1. Computer Systems that Learn. S a n M a t e o, C A: M o r g a n K a u f m a n n.
44. Xu, L.; Yan, P.; and Chang, T. 1989. Best first strategy for feature selection. In Ninth International Conference on Pattern Recognition, 706-708. IEEE Computer Society Press.