A new maximum-relevance criterion for significant gene selection

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

Volumn 4146 LNBI, Issue , 2006, Pages 71-80

  Kim, Young Bun ^a   Gao, Jean ^a   Michalak, Pawel ^b  

^a The University of Texas at Arlington   (United States)

^b UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; PATTERN RECOGNITION SYSTEMS; UNCERTAIN SYSTEMS;

EMERGING PATTERNS (EP); GENE SELECTION; MICROARRAY ANALYSIS;

GENES;

EID: 33750047889     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11818564_9     Document Type: Conference Paper

References (16)

1. R. Kohavi and G. John: Wrapper for feature subset selection. Arti.Intel. 97(1-2) (1997) 273-324
2. S. Das, Filters: Wrappers and a Boosting-Based Hybrid for Feature Selection. Proc. 18th Intl Conf. Mach. Learn. (2001) 74-81
3. H. Liu and L. Yu: Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Tran, on Know. and Data engi. 17(4) (2005) 491-502
4. H.Y. Chung, H. Liu, S. Brown, C. McMunn-Coffran, C.Y. Kao, and D. frank Hsu: Identifying Significant Genes from Microarray Data. Proc. of the fourth IEEE symp. on BIBE. 358 (2004).
5. C. Ding and H. Peng: Minimum redundancy feature selection from microarray gene expression data. J. of Bioinfo. and Comp. Bio. 3(2) (2005) 185-205
6. L. Yu and H. Liu: Efficient Feature Selection via Analysis of Relevance and Redundancy. J. of Mach. Learn. Rese. 5 (2004) 1205-1224
7. H. Peng, F. Long, and C. Ding. Feature Selection Based on Mutual Information:Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans. on Patt. anal, and mach, intel. 27(8) (2005) 1226-1238
8. G. Dong and J. Li: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. Proc. of the fifth ACM SIGKDD Inter. Conf. on Know.e Disc, and Data min. (1999) 43-52
9. J. Li, G. Dong and K. Ramamohanarao: Making Use of the Most Expressive Jumping Emerging Patterns for Classification. Know, and Info. Sys. 3(2) (2001) 131-145
10. J. Li and L. Wong: Identifying good diagnostic genes or genes groups from gene expression data by using the concept of emerging patterns. Bioinformatics. 18 (2002) 725-734, 1407-1408
11. U. Fayyad and K. Irani: Multi-interval discretization of continuous-valued attributes for classification learning. Proc. of the 13th Inter. J. Conf. on Arti. Intel. (1993) 1022-1029
12. P. Mitra, Murthy, and S.K. Pal: Unsupervised Feature Selection Using Feature Similarity. IEEE Tran, of Patt. anal, and mach, intel. 24(2) (2002) 301-312
13. L. Yu and H. Liu: Redundancy Based Feature Selection for Microarray Data. KDD'04. (2004) 22-25
14. C.W. Hsu, C.J. Lin: A comparison of methods for multi-class support vector machines. IEEE Trans. of Neural Networks. 13 (2002) 415-425
15. U. Alon, N. Barkai, D. Notterman et al: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. of the Nat. Acad. of Sciences. 96(10) (1999) 6745-6750
16. T.R. Golub, D.K. Slonim et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 286 (1999) 531-537