Fast feature selection using a simple estimation of distribution algorithm: A case study on splice site prediction

Bioinformatics

Volumn 19, Issue SUPPL. 2, 2003, Pages

  Saeys, Yvan ^a   Degroeve, Sven ^a   Aeyels, Dirk ^b   Van De Peer, Yves ^a   Rouzé, Pierre ^b  

^a DEPARTMENT OF PLANT SYSTEMS BIOLOGY   (Belgium)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

Estimation of Distribution Algorithms; Feature subset selection; Machine learning; Splice Site Prediction

Indexed keywords

ALGORITHM; ARABIDOPSIS; CONFERENCE PAPER; GENE SEQUENCE; GENETIC SELECTION; INTRON; MATHEMATICAL ANALYSIS; METHODOLOGY; NONHUMAN; PRIORITY JOURNAL;

ARABIDOPSIS THALIANA;

EID: 2942594395     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btg1076     Document Type: Conference Paper

References (28)

1. Bekkerman, R., El-Yaniv, R., Tishby, N. and Winter, Y. (2001) On feature distributional clustering for text categorization. In Proceedings of SIGIR-01, 24th ACM International Conference on Research and Development in Information Retrieval, pp. 146-153.
2. Boser, B., Guyon, I. and Vapnik, V.N. (1992) A training algorithm for optimal margin classifiers. In Haussler,D. (ed.), Proceedings of COLT. ACN Press, pp. 144-152.
3. Boz, O. (2002) Feature subset selection by using sorted feature relevance. In Proceedings of the International Conference on Machine Learning and Applications (ICMLA 2002).
4. Cantú-Paz, E. (2002) Feature subset selection by estimation of distribution algorithms. In Langdon, W.B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E. and Jonoska, N. (eds), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). Morgan Kaufmann, San Francisco, CA, pp. 754-761.
5. Degroeve, S., De Baets, B., Van de Peer, Y. and Rouzé, P. (2002) Feature subset selection for splice site prediction. Bioinformatics, 18, 75-83.
6. Duda, R.O. and Hart, P.E. (1973) Pattern classification and scene analysis. Wiley, New York.
7. Guyon, I., Western, J., Barnhill, S. and Vapnik, V.N. (2000) Gene selection for cancer classification using support vector machines. Machine Learning.
8. Hall, M.A. (1999) Correlation based feature selection for machine learning. Doctoral dissertation, Department of Computer Science, The University of Waikato, Hamilton, New Zealand.
9. Harik, G.R., Lobo, G.G. and Goldberg, D.E. (1998) The compact genetic algorithm. In Proceedings of the International Conference on Evolutionary Computation 1998 (ECEC '98), Piscataway, IEEE Service Center, NJ, pp. 523-528.
10. Hush, D. and Scovel, C. (2000) Polynomial-time decomposition for support vector machines. Technical report. Los Alamos National Laboratory, Los Alamos, NM 87545.
11. Inza, I., Larrañaga, G., Etxebarria, R. and Sierra, B. (1999) Feature subset selection by Bayesian networks based on optimization. Artificial Intelligence, 27, 143-164.
12. Joachims, T. (1998) Making large-scale support vector machine learning practical. In Schölkopf, B., Burges, C. and Smola, A. (eds), Advances in Kernel Methods: Support Vector Machines, MIT Press, Cambridge, MA, December.
13. Kohavi, R. and John, G. (1997) Wrappers for feature subset selection. Artificial Intelligence J., 97, 273-324.
14. Kudo, M. and Sklansky, J. (2000) Comparison of algorithms that select features for pattern classifiers. Pattern Recogn., 33, 25-41.
15. Langley, P. and Sage, S. (1994) Induction of selective Bayesian classifiers. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (1994). Morgan Kauffman, pp. 399-406.
16. Mathé, C., Sagot, M.F., Schiex, T. and Rouzé, P. (2002) Current methods of gene prediction, their strengths and weaknesses. Nucleic Acids Res., 30, 4103-4117.
17. McCallum, A. and Nigam, K. (1998) A comparison of event models for naive Bayes text classification. AAAI/ICML-98 Workshop on Learning for Text Categorization. AAAI Press, pp. 41-48.
18. Mühlenbein, H. and Paass, G. (1996) From recombination of genes to the estimation of distributions. Binary parameters. Parallel Problem Solving from Nature, PPSN IV, Lecture Notes in Computer Science, 1411, pp. 178-187.
19. Mühlenbein, H. (1996) The equation for response to selection and its use for prediction. Evolutionary Computation, 5, 303-346.
20. Larrañaga, P. and Lozano, J.A. (2001) Estimation of distribution algorithms. A New Tool for Evolutionary: Computation. Kluwer Academic Publishers.
21. Siedelecky, W. and Klansky, J. (1988) On automatic feature selection. Int. J. Pattern Recogn, 2, 197-220.
22. Snyder, E.E. and Stormo, G.D. (1995) Identification of protein regions in genomic DNA. J. Mol. Biol., 248, 1-18.
23. Sonnenburg, S. (2002) New Methods for Splice Site recognition, Diploma thesis, Humboldt-Universität zu Berlin.
24. Tishby, N., Pereira, F.C. and Bialek, W. (1999) The information bottleneck method. In Proceedings of the 37th Annual Allerton Conference on Communication, Control and Computation, pp. 368-377.
25. Vapnik, V.N. (1995) The nature of statistical learning theory. Springer.
26. Vafaie, H. and De Jong, K. (1993) Robust feature selection algorithms. In Proceedings Fifth International Conference on Tools with Artificial Intelligence, pp. 356-363.
27. Zhang, M.Q. (2002) Computational prediction of eukaryotic protein-coding genes. Nat. Rev. Genet., 3, 698-709.
28. Zhang, C.T. and Zhang, R. (2002) Evaluation of gene-finding algorithms by a content-balancing accuracy index. J. Biomol. Struct. Dyn., 19, 1045-1052.