Boost feature subset selection: A new gene selection algorithm for microarray dataset

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3992 LNCS - II, Issue , 2006, Pages 670-677

  Xu, Xian ^a   Zhang, Aidong ^a  

^a UNIVERSITY AT BUFFALO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARRAYS; GENES; PROBLEM SOLVING; SET THEORY;

BIOINFORMATICS; DATA SETS; DISCRIMINATIVE SCORES; FEATURE SUBSET SELECTION;

DATA RECORDING;

EID: 33746645146     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11758525_91     Document Type: Conference Paper

References (15)

1. U. Alon, N. Barkai, D.A. Notterman, K. Gish, S. Ybarra, D. Mack, and A.J. Levine. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissue probed by oligonucleolide arrays. Proc. Natl. Acad. Sci. U.S.A., 96(12):6745-50, 1999.
2. A. Ben-Dor, L. Bruhn, N. Friedman, I. Nachman, M. Schummer, and Z. Yakhini. Tissue classification with gene expression profiles, volume 7, pages 559-83, 2000.
3. T.H. Bø and I. Jonassen. New feature subset selection procedures for classification of expression profiles. Genome Biology, 3(4):research0017.1- 0017.11, 2002.
4. Leo Breiman. Bagging predictors. Machine Learning, 1996.
5. Yoav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. In in Proc. ICML 1996, 1996.
6. T. R. Golub et al. Molecular classifications of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286(5439):531-7, 1999.
7. J. Jaeger, R. Sengupta, and W. L. Ruzzo. Improved gene selection for classification of microarrays. In Proc. PSB, 2003.
8. Anil K. Jain, Robert P.W. Duin, and Jianchang Mao. Statistical pattern recognition: A review, IEEE Transactions on pattern analysis and Machine intelligence, 22(1):4-37, 2000.
9. D. Jiang, C. Tang, and A. Zhang. Cluster analysis for gene expression data: A survey. IEEE Transactions on Knowledge and Data Engineering, 16(11):1370-1386, 2004.
10. S. Ramaswamy, P. Tamayo, R. Rifkin, S Mukherjee, C.H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J.P. Mesirov, T. Poggio, W. Gerald, M. Loda, E. S. Lander, and T.R. Golub. Multiclass cancer diagnosis using tumor gene expression signatures. PNAS, 98(26):15149-15154, 2001.
11. Yuhang Wang, Fillia S. Makedon, James C. Ford, and Justin Pearlman. Hykgene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics, 21(8):1530-1537, 2005.
12. Y. Wu and A. Zhang. Feature selection for classifying high-dimensional numerical data. In IEEE Conference on Computer Vision and Pattern Recognition 2004, volume 2, pages 251-258, 2004.
13. E. P. Xing, M. I. Jordan, and R. M. Karp. Feature selection for high-dimensional genomic microarray data. In Proc. 18th International Conf. on Machine Learning, pages 601-608. Morgan Kaufmann, San Francisco, CA, 2001.
14. Xian Xu and Aidong Zhang. Virtual gene: Using correlations between genes to select informative genes on microarray datasets, LNCS Transactions on Computational Systems Biology II, LNBI 3680, pages 138-152, 2005.
15. L. Yu and H. Liu, Redundancy based feature selection for microarray data. In Proc. of SIGKDD, 2004.