Random subspace method for multivariate feature selection

Pattern Recognition Letters

Volumn 27, Issue 10, 2006, Pages 1067-1076

  Lai, Carmen ^a   Reinders, Marcel J T ^a   Wessels, Lodewyk ^a,b  

^a Department of Microelectronics   (Netherlands)

^b NETHERLANDS CANCER INSTITUTE   (Netherlands)

Author keywords

Feature selection; Random subspace method; Small sample size problem

Indexed keywords

ACOUSTIC NOISE; DATA ACQUISITION; ENCAPSULATION; PATTERN RECOGNITION; RELIABILITY; ROBUSTNESS (CONTROL SYSTEMS);

FEATURE SELECTION; RANDOM SUBSPACE METHOD; SMALL SAMPLE SIZE PROBLEM;

MULTIVARIABLE SYSTEMS;

EID: 33646258047     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2005.12.018     Document Type: Article

References (36)

1. Allwein, E., Schapire, R., Singer, Y., 2000. Reducing multiclass to binary: a unifying approach for margin classifiers. In: Kaufmann, M., (Ed.), Proc. 17th Internat. Conf. on Machine Learning, p. 9-16.
2. Ambroise C., and McLachlan G.J. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. USA 99 10 (2002) 6562-6566
3. Ben-Dor A., Bruhn L., Friedman N., Nachman I., Schummer M., and Yakhini Z. Tissue classification with gene expression profiles. Proc. 4th Ann. Internat. Conf. on Computational Molecular Biology (2000), ACM Press 54-64
4. Bhattacharyya C., Grate L.R., Rizki A., Radisky D., Molina F.J., Jordan M.I., Bissell M.J., and Mian I.S. Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data. Signal Process. 83 4 (2003) 729-743
5. Bo T., and Jonassen I. New feature subset selection procedures for classification of expression profiles. Genome Biol. 3 (2002)
6. Chow M.L., Moler I.S., and Mian E.J. Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol Genomics 5 (2001) 99-111
7. Dai D.Q., and Yuen P.C. Regularized discriminant analysis and its application to face recognition. Pattern Recognition 36 (2003) 845-847
8. de Veld D.C.G., Skurichina M., Witjes M.J.H., Duin R.P.W., Sterenborg D.J.C.M., Star W.M., and Roodenburg J.L.N. Clinical study for classification of benign, dysplastic, and malignant oral lesions using autofluorescence spectroscopy. J. Biomed. Opt. 9 5 (2004) 940-950
9. Dietterich T., and Bakiri G. Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. (1995) 263-286
10. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), John Wiley & Sons, Inc.
11. Dudoit S., Fridlyand J., and Speed T.P. Comparison of discrimination methods for the classification of tumors using gene expression data. J. Amer. Statist. Assoc. 97 457 (2002) 77-87
12. Duin, R.P.W., Juszczak, P., de Ridder, D., Paclík, P., Pekalska, E., Tax, D.M.J., 2004. PR-Tools 4.0, a Matlab toolbox for pattern recognition. Technical report, ICT Group, TU Delft, The Netherlands, January 2004. Available from: .
13. Ein-Dor L., Kela I., Getz G., Givol D., and Domany E. Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics 12 (2004)
14. Friedman J.H. Regularized discriminant analysis. J. Amer. Statist. Assoc. (1989)
15. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
16. Grate, L.R., Bhattacharyya, C., Jordan, M.I., Mian, I.S., 2002. Simultaneous classification and relevant feature identification in high-dimensional spaces. In: Workshop on Algorithms in Bioinformatics.
17. Guyon I., Weston J., and Barnhill S. Gene selection for cancer classification using support vector machines. Machine Learn. 46 (2002) 389-422
18. Ho, T.K., 1995. Random decision forests. In: 3rd Internat. Conf. on Document Analysis and Recognition, pp. 278-282.
19. Ho T.K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Machine Intell. 20 8 (1998) 832-844
20. Jain A., and Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Machine Intell. 19 2 (1997)
21. Khan J., Wei J.S., Ringner M., Saal L.H., Ladanyi M., Westermann F., Berthold F., Schwab M., Antonescu C.R., Peterson C., and Meltzer P.S. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Med. 7 6 (2001) 673-679
22. Kohavi, R., 1995. The power of decision tables. In: Proc. European Conf. on Machine Learning.
23. Kohavi G., and John R. Wrappers for feature subset selection. Artif. Intell. 97 (1997) 273-324
24. Li L., Weinberg C.R., Darden T.A., and Pedersen L.G. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the ga/knn method. Bioinformatics 17 12 (2001) 1131-1142
25. Paclík, P., Landgrebe, T.C.W., Duin, R.P.W., 2003. Prexp 2.0, a Matlab toolbox for evaluation of pattern recognition experiment. Technical report, ICT Group, TU Delft, The Netherlands, December.
26. Pudil P., Novovicova J., and Kittler J. Floating search methods in feature selection. PRL 15 (1994) 1119-1125
27. Raudys S., and Jain A. Small sample size effect in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Pattern Anal. Machine Intell. 13 3 (1991) 252-264
28. Skurichina, M., 2001. Stabilizing weak classifiers. Ph.D. thesis, Delft, Technical University.
29. Skurichina M., and Duin R.P.W. Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 5 (2002) 121-135
30. Tax, D.M.J., Duin, R.P.W., 2002. Using two-class classifiers for multiclass classification. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 2.
31. Tibshirani R., Hastie T., Narasimhan B., and Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. USA 99 10 (2002) 6567-6572
32. Trunk G.V. A problem af dimensionality: a simple example. IEEE Trans. Pattern Anal. Machine Intell. 1 3 (1979)
33. van 't Veer L.J., Dai H., van de Vijver M.J., He Yudong D., Hart A.A.M., Mao M., Peterse H.L., van der Kooy K., Marton M.J., Witteveen A.T., Schreiber G.J., Kerkhoven R.M., Roberts C., Linsley P.S., Bernards R., and Friend S.H. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415 (2002) 530-536
34. Wessels L.F.A., Reinders M.J.T., Hart A.A.M., Veenman C.J., Dai H., He Y.D., and van't Veer L.J. A protocol for building and evaluating predictors of disease state based on microarray data. Bioinformatics 21 19 (2005) 3755-3762
35. Xing, E., Jordan, M., Karp, R., 2001. Feature selection for high-dimensional genomic microarray data. In: Internat. Conf. on Machine Learning.
36. Xiong M., La W., Zhao J., Jin L., and Boerwinkle E. Feature (gene) selection in gene expression-based tumor classification. Mol. Genet. Metab. 73 (2001) 239-247