Fast-ensembles of minimum redundancy feature selection

Studies in Computational Intelligence

Volumn 373, Issue , 2011, Pages 75-95

  Schowe, Benjamin ^a   Morik, Katharina ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 80054756406     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-22910-7_5     Document Type: Article

References (20)

1. Bontempi, G., Meyer, P.E.: Causal filter selection in microarray data. In: Fürnkranz, J., Joachims, T. (eds.) Proc. the 27th Int. Conf.Machine Learning, Haifa, Israel, pp. 95-102. Omnipress, Madison (2010)
2. Breiman, L.: Bagging predictors. Machine Learning 24, 123-140 (1996) (Pubitemid 126724382)
3. Breiman, L.: Random forests. Machine Learning 45, 5-32 (2001) (Pubitemid 32933532)
4. Ding, C.H.Q., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. In: Proc. the 2nd IEEE Comp. Society Bioinformatics Conf., Stanford, CA, pp. 523-529. IEEE Comp. Society, Los Alamitos (2003)
5. Fox, R.J., Dimmic, M.W.: A two-sample Bayesian t-test for microarray data. BMC Bioinformatics 7 (2006)
6. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119-139 (1997) (Pubitemid 127433398)
7. Gulgezen, G., Cataltepe, Z., Yu, L.: Stable and accurate feature selection. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 455-468. Springer, Heidelberg (2009)
8. Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Langley, P. (ed.) Proc. the 17th Int. Conf. Machine Learning, Stanford, CA, pp. 359-366. Morgan Kaufmann, San Francisco (2000)
9. Han, Y., Yu, L.: A variance reduction framework for stable feature selection. In: Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) Proc. the 10th IEEE Int. Conf. Data Mining, Sydney, Australia, pp. 206-215. IEEE Computer Society, Los Alamitos (2010)
10. Jurman, G., Merler, S., Barla, A., Paoli, S., Galea, A., Furlanello, C.: Algebraic stability indicators for ranked lists in molecular profiling. Bioinformatics 24, 258-264 (2008)
11. Kalousis, A., Prados, J., Hilario, M.: Stability of feature selection algorithms: a study on high-dimensional spaces. Knowledge and Inf. Syst. 12, 95-116 (2007)
12. Koh, J.L.Y., Li Lee, M., Hsu, W., Lam, K.-T.: Correlation-based detection of attribute outliers. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 164-175. Springer, Heidelberg (2007)
13. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97, 273-324 (1997) (Pubitemid 127401107)
14. Kuncheva, L.I.: A stability index for feature selection. In: Devedzic, V. (ed.) IASTED Int. Conf. Artif. Intell. and Appl., Innsbruck, Austria, pp. 421-427. ACTA Press, Calgary (2007)
15. Michalak, K., Kwasnicka, H.: Correlation-based feature selection strategy in neural classification. In: Proc. the 6th Int. Conf. Intell. Syst. Design and Appl., Jinan, China, pp. 741-746. IEEE Comp. Society, Los Alamitos (2006) (Pubitemid 47178925)
16. Saeys, Y., Abeel, T., Van de Peer, Y.: Robust feature selection using ensemble feature selection techniques. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 313-325. Springer, Heidelberg (2008)
17. Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. the National Academy of Sciences of the United States of America 98, 5116-5121 (2001) (Pubitemid 32397092)
18. Vapnik, V.: Statistical learning theory. Wiley, Chichester (1998)
19. Xu, X., Zhang, A.: Boost feature subset selection: A new gene selection algorithm for microarray dataset. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3992, pp. 670-677. Springer, Heidelberg (2006) (Pubitemid 44152571)
20. Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Machine Learning Research 5, 1205-1224 (2004)