F-score with pareto front analysis for multiclass gene selection

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

Volumn 5483 LNCS, Issue , 2009, Pages 56-67

  Mundra, Piyushkumar A ^a   Rajapakse, Jagath C ^a,b,c  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c SINGAPORE MIT ALLIANCE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

CANCER GENE EXPRESSION; CLASSIFICATION PERFORMANCE; F-SCORE; FILTER CRITERIA; FRONTAL ANALYSIS; GENE SELECTION; MULTI-CLASS; MULTI-CLASS CANCER CLASSIFICATION; PARETO FRONT; RANKING CRITERION; SUM OF SQUARES;

GENE EXPRESSION; MINING; ROBOT LEARNING;

BIOINFORMATICS;

EID: 67650665334     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01184-9_6     Document Type: Conference Paper

References (27)

1. Inza, I., Larranaga, P., Blanco, R., Cerrolaza, A.: Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial Intelligence Medicine 31, 91-103 (2004) (Pubitemid 38823034)
2. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinformatics Computational Biology 3, 185-205 (2005) (Pubitemid 40561382)
3. Ooi, C., Chetty, M., Teng, S.: Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data. BMC Bioinformatics 7, 320-339 (2006) (Pubitemid 44399877)
4. Kai-Bo, D., Rajapakse, J., Wang, H., Azuaje, F.: Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans. Nanobioscience 4, 228-234 (2005) (Pubitemid 41441412)
5. Mundra, P., Rajapakse, J.: SVM-RFE with relevancy and redundancy criteria for gene selection. In: Rajapakse, J.C., Schmidt, B., Volkert, L.G. (eds.) PRIB 2007. LNCS (LNBI), vol.4774, pp. 242-252. Springer, Heidelberg (2007)
6. Li, T., Zhang, C., Ogihara, M.: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20(15), 2429-2437 (2004) (Pubitemid 39429915)
7. Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. J. American Statistical Association 97(457), 77-86 (2002)
8. Chen, D., Liu, Z., Ma, X., Hua, D.: Selecting genes by test statistics. J. Biomedicine and Biotechnology 2, 132-138 (2005) (Pubitemid 41627869)
9. Cho, J.-H., Lee, D., Park, J.H., Lee, I.-B.: New gene selection method for classification of cancer subtypes considering within-class variation. FEBS Letters 551, 3-7 (2003) (Pubitemid 37281318)
10. Zhou, X., Tuck, T.P.: MSVM-RFE: Extensions of SVM-RFE for multiclass gene selection on dna microarray data. Bioinformatics 23(9), 1106-1114 (2007) (Pubitemid 47050562)
11. Duan, K.B., Rajapakse, J., Nguyen, M.: One-versus-one and one-versus-all multiclass SVM-RFE for gene selection in cancer classification. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds.) EvoBIO 2007. LNCS, vol.4447, pp. 47-56. Springer, Heidelberg (2007)
12. Jirapech-Umpai, T., Aitken, S.: Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics 6, 148-1138 (2005) (Pubitemid 41357999)
13. Ooi, C.H., Tan, P.: Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 19(1), 37-44 (2003) (Pubitemid 36150179)
14. Xuan, J., Wang, Y., Dong, Y., Feng, Y., et al.: Gene selection for multiclass prediction by weighted fisher criterion. EURASIP J. Bioinformatics and Systems Biology 2007(article id 64628) (2007) (Pubitemid 47116867)
15. Forman, G.: A pitfall and solution in multi-class feature selection for text classification. In: Proceedings of the twenty-first international conference on Machine learning (2004) (Pubitemid 40290821)
16. Hero, A., Fleury, G.: Pareto-optimal methods for gene ranking. J. VLSI Signal Processing 38, 259-275 (2004)
17. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation 6(2), 182-197 (2002) (Pubitemid 34555372)
18. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S.: Others: Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences 98(26), 15149-15154 (2001) (Pubitemid 34013984)
19. Bhattacharjee, A., Richards, W., Staunton, J., Li, C.: Others: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of the National Academy of Sciences 98(24), 13790-13795 (2001) (Pubitemid 33115977)
20. Armstrong, S., Staunton, J., Silverman, L., Pieters, R., et al.: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics 30(1), 41-47 (2002) (Pubitemid 34886688)
21. Ross, D.T., Scherf, U., Eisen, M.B., Perou, C., et al.: Systematic variation in gene expression patterns in human cancer cell. Nature Genetics 24(3), 227-235 (2000) (Pubitemid 30132189)
22. Culhane, A., Perriere, G., Higgins, D.: Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics 4(1), 59 (2003) (Pubitemid 38751211)
23. Gorodkin, J.: Comparing two K-category assignment by a K-category correlation coefficient. Computational Biology and Chemistry 28, 367-374 (2004)
24. Liu, J., Kang, S., Tang, C., Ellis, L.B., Li, T.: Meta-prediction of protein subcellular localization with reduced voting. Nucleic Acid Research 35(15), e96 (2007) (Pubitemid 47394212)
25. Chang, C., Lin, C.: Libsvm: A library for support vector machines (2001), www.csie.ntu.edu.tw/~cjlin/libsvm
26. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)
27. Jensen, M.: Reducing run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. IEEE Trans. Evolutionary Computation 7(5), 503-515 (2003)