On the effectiveness of discretization on gene selection of microarray data

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2010, Pages

  Bolon Canedo V ^a   Sanchez Marono N ^a   Alonso Betanzos, A ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE SELECTION; LEARNING SYSTEMS;

DISCRETIZATIONS; DNA MICROARRAY DATA; FEATURE SELECTION METHODS; FILTER METHOD; GENE SELECTION; GENES EXPRESSION; MACHINE-LEARNING; MICROARRAY DATASET; MICROARRAYS DATA; SMALL SAMPLE SIZE;

GENE EXPRESSION;

EID: 79959421384     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2010.5596825     Document Type: Conference Paper

References (35)

1. A. Blumer, A. Ehrenfeucht, D. Haussler and M.K. Warmuth, Occam's Razor, Journal of Information Processing Letters, vol. 24, no. 6, pp. 377-380, 1987.
2. Y. Wang, I.V. Tetko, M.A. Hall, E. Frank, A. Facius, K.F.X. Mayer and H.W. Mewes, Gene Selection from Microarray Data for Cancer Classification A Machine Learning Approach, Journal of Computational Biology and Chemistry, vol. 29, no. 1, pp. 37-46, 2005.
3. R. Ruiz, J.C. Riquelme and J.S. Aguilar-Ruiz, Incremental Wrapper-Based Gene Selection from Microarray Data for Cancer Classification, Journal of Pattern Recognition, vol. 39, no. 12, pp. 2383-2392, 2006.
4. Y. Saeys, I. Inza and P. Larrañaga, A Review of Feature Selection Techniques in Bioinformatics, Journal of Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007
5. C. Ambroise and G.J. McLachlan, Selection Bias in Gene Extraction on the Basis of Microarray Gene-Expression Data, In Proceedings of the National Academy of Sciences, vol. 99, no. 10, pp. 6562-6566, 2002
6. C.J. Alonso-González, Q.I. Moro, O.J. Prieto and M.A. Simón, Selecting Few Genes for Microarray Gene Expression Classification, In Proceedings of Conferencia de la Asociación Española de Inteligencia Artificial, pp. 21-30, 2009
7. I. Guyon, S. Gunn, M. Nikravesh and L. Zadeh, Feature Extraction. Foundations and Applications, Springer, 2006
8. T.R. Golub, D.K Stomin, P. Tamayo et al., Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Journal of Science, vol. 286, no. 5439, pp. 531-537, 1999
9. A. Jain and D. Zongker, Feature Selection: Evaluation, Application, and Small Sample Performance, Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 153-158, 1997
10. H. Liu and R. Setiono, Feature Selection via Discretization, Journal of IEEE Transactions on Knowledge and Data Engineering, pp. 642-645, 1997
11. H. Liu, F. Hussain, C.L. Tan and M. Dash, Discretization: An Enabling Technique, Journal of Data Mining and Knowledge Discovery, vol. 6, no. 4, pp. 393-423, 2002
12. I.H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005 http://www.cs.waikato.ac.nz/ml/weka/ [Last access: January 2010]
13. D. Janssens, T. Brijs, K. Vanhoof and G. Wets, Evaluating the Performance of Cost-based Discretization versus Entropy and Error-based Discretization, Journal of Computers and Operations Research, vol. 33, no. 11, pp. 3107-3123, Elsevier, 2006
14. V. Bolón-Canedo, N. Sánchez-Maroño and A. Alonso-Betanzos, A Combination of Discretization and Filter Methods for Improving Classification Performance in KDD Cup 99 Dataset, In Proceedings of International Joint Conference on Neural Networks, pp. 359-366, 2009
15. U.M. Fayyad and K.B. Irani, Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning, Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022-1029, Morgan Kaufmann, 1993
16. Y. Yang and G.I. Webb, Proportional k-Interval Discretization for Naive-Bayes Classifiers, In Proceedings of the 12th European Conference on Machine Learning, pp. 564-575, 2001
17. M.A. Hall, Correlation-based Feature Selection for Machine Learning, PhD thesis, University of Waikato, Hamilton, New Zealand, 1999
18. Z. Zhao and H. Liu, Searching for Interacting Features, In Proceedings of International Joint Conference on Artificial Intelligence, 1991, pp. 1156-1167
19. M. Dash and H. Liu, Consistency-based Search in Feature Selection, Journal of Artificial Intelligence, vol. 151, no. 1-2, pp. 155-176, 2003
20. T.M. Mitchell, Machine Learning, McGraw-Hill, New York, 1997
21. D. Michie, Personal Models of Rationality, Journal of Statistical Planning and Inference, vol. 25, no. 3, pp. 381-399, 1990
22. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993
23. I. Rish, An Empirical Study of the Naive Bayes Classifier. Proceedings of IJCAI-01 Workshop on Empirical Methods in Artificial Intelligence, vol. 335, 2001
24. D.W. Aha, D. Kibler and M.K. Albert, Instance-Based Learning Algorithms, Journal of Machine Learning, vol. 6, no. 1, pp. 37-66, 1991
25. K. Ridge, Kent Ridge Bio-Medical Dataset, 2009 http://datam. i2r.a-star.edu.sg/datasets/krbd [Last access: January 2010]
26. Broad Institute. Cancer Program Data Sets http://www. broadinstitute.org/cgi-bin/cancer/datasets.cgi [Last access: January 2010]
27. S.L. Pomeroy, P. Tamayo, M. Gaasenbeek et al., Prediction of Central Nervous System Embryonal Tumour Outcome Based on Gene Expression, Journal of Nature, vol. 415, no. 6870, pp. 436-442, 2002
28. A.A. Alizadeh, M.B. Elisen, R.E. Davis et al., Distinct Types of Diffuse Large B-cell Lymphoma Identified by Gene Expression Profiling, Journal of Nature, vol. 403, no. 6769, pp. 503-511, 2000
29. U. Alon, N. Barkai, D.A. Notterman, K. Gish et al., Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays, Proceedings of the National Academy of Sciences, vol. 96, no. 12, pp. 6745-6750, 1999
30. D. Singh, P.G. Febbo, K. Ross, D.G. Jackson et al., Gene Expression Correlates of Clinical Prostate Cancer Behavior, Journal of Cancer Cell, vol. 1, no. 2, pp. 203-209, 2002
31. G.J. Gordon, R.V. Jenson, L.L. Hsiao et al., Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gene Expression Ratios in Lung Cancer and Mesothelioma, Journal of Cancer Research, vol. 62, no. 17, pp. 4963-4967, 2002
32. E.F. Petricoin, A.M. Ardekani, B.A. Hitt et al., Use of Proteomic Patterns in Serum to Identify Ovarian Cancer, Journal of the Lancet, vol. 359, no. 9306, pp. 572-577. 2002
33. L.J. Van't Veer, H. Dai, M.J. Van de Vijver et al., Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer, Journal of Nature, vol. 415, no. 6871, pp. 530-536, 2002
34. S. Ramaswamy, P. Tamayo, R. Rifkin et al., Multiclass Cancer Diagnosis Using Tumor Gene Expression Signatures, In Proceedings of the National Academy of Sciences, vol. 98, no. 26, pp. 15149-15154, 2001
35. T. Li, C. Zhang and M. Ogihara, A Comparative Study of Feature Selection and Multiclass Classification Methods for Tissue Classification Based on Gene Expression, Journal of Bioinformatics, vol. 20, no. 15, pp. 2429-2437, 2004