Non-parametric classifier-independent feature selection

Pattern Recognition

Volumn 39, Issue 5, 2006, Pages 737-746

  Abe, Naoto ^a   Kudo, Mineichi ^a  

^a HOKKAIDO UNIVERSITY   (Japan)

Author keywords

Bayes classifier; Classifier independent feature selection; Garbage feature; Non parametric; Two stage feature selection

Indexed keywords

DATA STRUCTURES; SET THEORY;

BAYES CLASSIFIERS; GARBAGE FEATURE; NON-PARAMETRIC; TWO-STAGE FEATURE SELECTION;

FEATURE EXTRACTION;

EID: 33244485264     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.11.007     Document Type: Article

References (41)

1. P. Somol, P. Pudil, and J. Kittler Fast branch & bound algorithms for optimal feature selection IEEE Trans. Pattern Anal. Mach. Intell. 26 2004 900 912
2. I.S. Oh, J.S. Lee, and B.R. Moon Hybrid genetic algorithms for feature selection IEEE Trans. Pattern Anal. Mach. Intell. 26 2004 1424 1437
3. P. Pudil, J. Novovičová, and J. Kittler Floating search methods in feature selection Pattern Recognition Lett. 15 1994 1119 1125
4. B. Yu, and B. Yuan A more efficient branch and bound algorithm for feature selection Pattern Recognition 26 1993 883 889
5. J.H. Yang, and V. Honavar Feature subset selection using a genetic algorithm IEEE Intell. Syst. 13 1998 44 49
6. M. Sebban, and R. Nock A hybrid filter/wrapper approach of feature selection using information theory Pattern Recognition 35 2002 835 846
7. K. Kira, and L. Rendell A practical approach to feature selection D. Sleeman P. Edwards Proceedings of 9th International Conference on Machine Learning 1992 Aberdeen Scotland 249 256
8. I. Kononenko Estimating attributes analysis and extensions of RELIEF F. Bergadano L. de Raedt Proceedings of the European Conference on Machine Learning Catania, Italy 1994 171 182
9. R. Kohavi, and G.H. John Wrappers for feature subset selection Artif. Intell. 97 1997 273 324
10. H. Almuallim, and T.G. Dietterich Learning with many irrelevant features M.T. Maybury Proceedings of the 9th National Conference on Artificial Intelligence Anaheim, California 1991 547 552
11. D. Koller, and M. Sahami Toward optimal feature selection L. Saitta Proceedings of the 13th International Conference on Machine Learning Bari, Italy 1996 284 292
12. N. Kwak, and C.H. Choi Input feature selection by mutual information based on parzen window IEEE Trans. Pattern Anal. Mach. Intell. 24 2002 1667 1671
13. F.J. Ferri, P. Pudil, M. Hatef, and J. Kittler Comparative study of techniques for large-scale feature selection E.S. Gelsema L.N. Kanal Pattern Recognition in Practice IV 1994 403 413
14. A.L. Blum, and P. Langley Selection of relevant features and examples in machine learning Artif. Intell. 97 1997 245 271
15. A. Jain, and D. Zongker Feature selection: evaluation, application, and small sample performance IEEE Trans. Pattern Anal. Mach. Intell. 19 1997 153 158
16. M. Kudo, and J. Sklansky Comparison of algorithms that select features for pattern classifiers Pattern Recognition 33 2000 25 41
17. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer New York
18. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J. Mach. Learn. Res. 3 2003 1157 1182
19. A. Rakotomamonjy Variable selection using SVM-based Criteria J. Mach. Learn. Res. 3 2003 1357 1370
20. J. Bi, K.P. Bennett, M. Embrechts, C.M. Breneman, and M. Song Dimensionality reduction via sparse support vector machines J. Mach. Learn. Res. 3 2003 1229 1243
21. J. Weston, A. Elisseeff, B. Scholkopf, and M. Tipping Use of the zero-norm with linear models and kernel methods J. Mach. Learn. Res. 3 2003 1439 1461
22. J. Brank, M. Grobelnik, N. Milić-Frayling, and D. Mladenić Feature selection using support vector machines Proceedings of the 3rd International Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields Bologna, Italy 2002
23. J. Novovičová, P. Pudil, and J. Kittler Divergence based feature selection for multimodal class densities IEEE Trans. Pattern Anal. Mach. Intell. 18 1996 218 223
24. M. Kudo, and M. Shimbo Feature selection based on the structural indices of categories Pattern Recognition 26 1993 891 901
25. H.J. Holz, M.H. Loew, Relative feature importance: a classifier- independent approach to feature selection, in: E.S. Gelsema, L.N. Kanal (Eds.), Pattern Recognition Pract. 4 (1994) 473-487.
26. S. Singh PRISM - A novel framework for pattern recognition Pattern Anal. Appl. 6 2003 134 149
27. L. Devroye, and G. Lugosi Combinatorial Methods in Density Estimation 2000 Springer Berlin
28. K. Fukunaga Introduction to Statistical Pattern Recognition second ed. 1990 Morgan Kaufmann Los Altos, CA
29. K. Fukunaga, and L.D. Hostetler The estimation of the gradient of a density function, with applications in pattern recognition IEEE Trans. Inf. Theory IT-21 1975 32 40
30. I. Tomek Two modifications of CNN IEEE Trans. Syst. Man Commun. 6 1976 769 772
31. K.R. Gabriel, and R.R. Sokal A new statistical approach to geographic variation analysis Syst. Zool. 18 1969 259 278
32. Y. Park, and J. Sklansky Automated design of multiple-class piecewise linear classifiers J. Classifications 6 1989 195 222
33. M. Kudo, and J. Sklansky Classifier-independent feature selection for two-stage feature selection A. Amin D. Dori P. Pudil H. Freeman Proceedings of the Joint IAPR International Workshops SSPR'98 and SPR'98 Sydney, Australia 1998 548 554
34. K. Fukunaga, and L.D. Hostetler Optimization of k -nearest neighbor density estimates IEEE Trans. Inf. Theory IT-19 1973 320 326
35. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, Irvine, CA: University of California, Department of Information and Computer Science, 1998 [ http://www.ics.uci.edu/~mlearn/MLRepository.html ].
36. N. Abe, M. Kudo, J. Toyama, and M. Shimbo A divergence criterion for classifier-independent feature selection F.J. Ferri J.M. Inesta A. Amin P. Pudil Proceedings of Joint IAPR International Workshops SSPR2000 and SPR2000 Alicante, Spain August/September, 2000 668 676
37. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via the EM algorithm J. Roy. Statist. Soc. 39 1977 1 38
38. N. Ichimura Robust clustering based on a maximum likelihood method for estimation of the suitable number of clusters Trans. Inst. Electron. Inf. Commun. Eng. J78-D-II 8 1995 1184 1195 (in Japanese)
39. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann San Mateo, CA
40. M. Kudo, S. Yanagi, and M. Shimbo Construction of class regions by a randomized algorithm: a randomized subclass method Pattern Recognition 29 1996 581 588
41. R. Collobert, and S. Bengio SVMTorch: support vector machines for large-scale regression problems J. Mach. Learn. Res. 1 2001 143 160