Scalability analysis of mRMR for microarray data

ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence

Volumn 1, Issue , 2014, Pages 380-386

  Rego Fernández, Diego ^a   Bolón Canedo, Verónica ^a   Alonso Betanzos, Amparo ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

Feature selection; Machine Learning; Microarray; Scalability

Indexed keywords

LEARNING SYSTEMS; MACHINE LEARNING; MICROARRAYS; SCALABILITY;

FEATURE SELECTION METHODS; FILTER METHOD; LEARNING METHODS; MICROARRAY DATA; MICROARRAY DATA SETS; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; SCALABILITY ANALYSIS; SYNTHETIC DATABASE;

FEATURE EXTRACTION;

EID: 84902352300     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5220/0004807703800386     Document Type: Conference Paper

References (15)

1. Belanche, L. And Gonzalez, F. (2011). Review and evaluation of feature selection algorithms in synthetic problems. ArXiv preprint arXiv:1101.2320.
2. Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M. K. (1987). Occam's razor. Information processing letters, 24(6):377-380.
3. Bolon-Canedo, V., Sanchez-Marono, N., and Alonso-Betanzos, A. (2013). A review of feature selection methods on synthetic data. Knowledge and information systems, 34(3):483-519.
4. Díaz-Uriarte, R. And De Andres, S. A. (2006). Gene selection and classification of microarray data using random forest. BMC bioinformatics, 7(1):3.
5. El Akadi, A., Amine, A., El Ouardighi, A., and Aboutajdine, D. (2011). A two-stage gene selection scheme utilizing mRMR filter and ga wrapper. Knowledge and Information Systems, 26(3):487-500.
6. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M.,Mesirov, J. P., Coller, H., Loh,M. L., Downing, J. R., Caligiuri, M. A., et al. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286(5439):531-537.
7. Guyon, I. (2006). Feature extraction: foundations and applications, volume 207. Springer.
8. Jain, A. And Zongker, D. (1997). Feature selection: Evaluation, application, and small sample performance. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(2):153-158.
9. Mundra, P. A. And Rajapakse, J. C. (2010). Svm-rfe with mrmr filter for gene selection. NanoBioscience, IEEE Transactions on, 9(1):31-37.
10. Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information criteria of maxdependency, max-relevance, and min-redundancy. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(8):1226-1238.
11. Saeys, Y., Inza, I., and Larranaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19):2507-2517.
12. Sonnenburg, S., Franc, V., Yom-Tov, E., and Sebag, M. (2008). Pascal large scale learning challenge. In 25th International Conference on Machine Learning (ICML2008) Workshop. J. Mach. Learn. Res, volume 10, pages 1937-1953.
13. Zhang, M.-L., Pena, J. M., and Robles, V. (2009). Feature selection for multi-label naive bayes classification. Information Sciences, 179(19):3218-3229.
14. Zhang, Y., Ding, C., and Li, T. (2008). Gene selection algorithm by combining ReliefF and mRMR. BMC genomics, 9(Suppl 2):S27.
15. Zhu, Z., Ong, Y.-S., and Zurada, J.M. (2010). Identification of full and partial class relevant genes. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, 7(2):263-277.