Consistency-based search in feature selection

Artificial Intelligence

Volumn 151, Issue 1-2, 2003, Pages 155-176

  Dash, Manoranjan ^a   Liu, Huan ^b  

^a Northwestern University   (United States)

^b Arizona State University   (United States)

Author keywords

Branch and bound; Classification; Evaluation measures; Feature selection; Random search; Search strategies

Indexed keywords

ALGORITHMS; HEURISTIC METHODS; OPTIMIZATION; SIMULATED ANNEALING;

CONSISTENCY-BASED SEARCH;

ARTIFICIAL INTELLIGENCE;

EID: 0242302657     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(03)00079-1     Document Type: Article

References (46)

1. Almuallim H., Dietterich T.G. Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence. 69:(1-2):1994;279-305.
2. Bell D.A., Wang H. A formalism for relevance and its application in feature subset selection. Machine Learning. 41:2000;175-195.
3. Ben-Bassat M. Pattern recognition and reduction of dimensionality. Krishnaiah P.R., Kanal L.N. Handbook of Statistics. 1982;773-791 North-Holland, Amsterdam.
4. Blake C.L., Merz C.J. UCI repository of machine learning databases, 1998 http://www.ics.uci.edu/̃mlearn/MLRepository.html.
5. Blum A.L., Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence. 97:1997;245-271.
6. Blumer A., Ehrenfeucht A., Haussler D., Warmuth M.K. Occam's razor. Shavlik J.W., Dietterich T.G. Readings in Machine Learning. 1990;201-204 Morgan Kaufmann, San Mateo, CA.
7. Brassard G., Bratley P. Fundamentals of Algorithms. 1996;Prentice Hall, Englewood Cliffs, NJ.
8. Cardie C. Using decision trees to improve case-based learning. Proceedings of Tenth International Conference on Machine Learning, Amherst, MA. 1993;25-32.
9. Das S. Filters, wrappers and a boosting-based hybrid for feature selection. Proceedings of the Eighteenth International Conference on Machine Learning (ICML), Williamstown, MA. 2001;74-81.
10. Dash M. Feature selection via set cover. Proceedings of IEEE Knowledge and Data Engineering Exchange Workshop, Newport, CA. 1997;165-171 IEEE Computer Society.
11. Dash M., Liu H. Feature selection methods for classification. Intelligent Data Analysis: An Internat. J. 1:(3):1997.
12. Dash M., Liu H. Hybrid search of feature subsets. Proceedings of Pacific Rim International Conference on Artificial Intelligence (PRICAI-98), Singapore. 1998;238-249.
13. Dash M., Liu H., Motoda H. Consistency based feature selection. Proceedings of Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Kyoto, Japan. 2000;98-109.
14. Devijver P.A., Kittler J. Pattern Recognition: A Statistical Approach. 1982;Prentice Hall, Englewood Cliffs, NJ.
15. J. Doak, An evaluation of feature selection methods and their application to computer security, Technical Report, University of California, Department of Computer Science, Davis, CA, 1992.
16. Hall M.A. Correlation-based feature selection for discrete and numeric class machine learning. Proceedings of Seventeenth International Conference on Machine Learning (ICML), Stanford, CA. 2000;359-366 Morgan Kaufmann, San Mateo, CA.
17. Inza I., Larranaga P., Etxeberria R., Sierra B. Feature subset selection by Bayesian network-based optimization. Artificial Intelligence. 123:2000;157-184.
18. John G.H., Kohavi R., Pfleger K. Irrelevant features and the subset selection problem. Proceedings of the Eleventh International Conference on Machine Learning, New Brunswick, NJ. 1994;121-129.
19. Johnson D.S. Approximation algorithms for combinatorial problems. J. Comput. System Sci. 9:1974;256-278.
20. Kira K., Rendell L.A. The feature selection problem: Traditional methods and a new algorithm. Proceedings of AAAI-92, San Jose, CA. 1992;129-134.
21. R. Kohavi, Wrappers for performance enhancement and oblivious decision graphs, PhD Thesis, Department of Computer Science, Stanford University, CA, 1995.
22. Kohavi R., John G.H. Wrappers for feature subset selection. Artificial Intelligence. 97:(1-2):1997;273-324.
23. Koller D., Sahami M. Toward optimal feature selection. Proceedings of International Conference on Machine Learning, Bari, Italy. 1996;284-292.
24. Kononenko I. Estimating attributes: Analysis and extension of RELIEF. Proceedings of European Conference on Machine Learning, Catania, Italy. 1994;171-182.
25. Liu H., Hussain F., Tan C.L., Dash M. Discretization: An enabling technique. J. Data Mining Knowledge Discovery. 6:(4):2002;393-423.
26. Liu H., Motoda H. Feature Extraction, Construction and Selection: A Data Mining Perspective. 1998;Kluwer Academic, Boston, MA.
27. Liu H., Motoda H. Feature Selection for Knowledge Discovery and Data Mining. 1998;Kluwer Academic, Dordrecht.
28. Liu H., Motoda H., Dash M. A monotonic measure for optimal feature selection. Proceedings of European Conference on Machine Learning, Chemnitz, Germany. 1998;101-106.
29. Liu H., Setiono R. Feature selection and classification - A probabilistic wrapper approach. Proceedings of Ninth International Conference on Industrial and Engineering Applications of AI and ES. 1996;419-424.
30. Liu H., Setiono R. A probabilistic approach to feature selection - A filter solution. Proceedings of International Conference on Machine Learning, Bari, Italy. 1996;319-327.
31. Modrzejewski M. Feature selection using rough sets theory. Brazdil P.B. Proceedings of the European Conference on Machine Learning, Vienna, Austria. 1993;213-226.
32. Moore A.W., Lee M.S. Efficient algorithms for minimizing cross validation error. Proceedings of Eleventh International Conference on Machine Learning, New Brunswick, NJ. 1994;190-198 Morgan Kaufmann, San Mateo, CA.
33. Mucciardi A.N., Gose E.E. A comparison of seven techniques for choosing subsets of pattern recognition. IEEE Trans. Comput. C-20:1971;1023-1031.
34. Narendra P.M., Fukunaga K. A branch and bound algorithm for feature selection. IEEE Trans. Comput. C-26:(9):1977;917-922.
35. Oliveira A.L., Vincentelli A.S. Constructive induction using a non-greedy strategy for feature selection. Proceedings of Ninth International Conference on Machine Learning, Aberdeen, Scotland. 1992;355-360 Morgan Kaufmann, San Mateo, CA.
36. Quinlan J.R. C4.5: Programs for Machine Learning. 1993;Morgan Kaufmann, San Mateo, CA.
37. T.W. Rauber, Inductive pattern classification methods - Features - Sensors, PhD Thesis, Department of Electrical Engineering, Universidade Nova de Lisboa, 1994.
38. Schlimmer J.C. Efficiently inducing determinations: A complete and systematic search algorithm that uses optimal pruning. Proceedings of Tenth International Conference on Machine Learning, Amherst, MA. 1993;284-290.
39. Segen J. Feature selection and constructive inference. Proceedings of Seventh International Conference on Pattern Recognition, Montreal, QB. 1984;1344-1346.
40. Sheinvald J., Dom B., Niblack W. A modelling approach to feature selection. Proceedings of Tenth International Conference on Pattern Recognition, Atlantic City, NJ, Vol. 1. 1990;535-539.
41. Siedlecki W., Sklansky J. On automatic feature selection. Internat. J. Pattern Recognition Artificial Intelligence. 2:1988;197-220.
42. Skalak D.B. Prototype and feature selection by sampling and random mutation hill-climbing algorithms. Proceedings of Eleventh International Conference on Machine Learning, New Brunswick, NJ. 1994;293-301 Morgan Kaufmann, San Mateo, CA.
43. Soucy P., Mineau G.W. A simple feature selection method for text classification. Proceedings of IJCAI-01, Seattle, WA. 2001;897-903.
44. Vafaie H., Imam I.F. Feature selection methods: Genetic algorithms vs. greedy-like search. Proceedings of International Conference on Fuzzy and Intelligent Control Systems. 1994.
45. Watanabe S. Pattern Recognition: Human and Mechanical. 1985;Wiley Interscience, New York.
46. A. Zell, et al., Stuttgart Neural Network Simulator (SNNS), User Manual, Version 4.1, Technical Report, 1995.