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12. Grzymala-Busse, J.W.: A new version of the rule induction system LERS. Fundam. Inform.
31 , 27-39 (1997)
13. Grzymala-Busse, J.W.: Mining numerical data—a rough set approach. In: Proceedings of the
RSEISP'2007, the International Conference of Rough Sets and Emerging Intelligent Systems
Paradigms, pp. 12-21 (2007)
14. Grzymala-Busse, J.W.: Rule induction. In: Maimon, O., Rokach, L. (eds.) Data Mining and
Knowledge Discovery Handbook, 2nd edn, pp. 249-265. Springer, Berlin (2010)
15. Grzymala-Busse, J.W.: An empirical comparison of rule induction using feature selection with
the LEM2 algorithm. In: Greco, S., Bouchon-Meunier, B.B., Coletti, G., Fedrizzi, M.M.,
Matarazzo, B., Yager, R.R. (eds.) Communications in Computer and Information Science, vol.
297, pp. 270-279. Springer (2012)
16. Grzymala-Busse, J.W., Grzymala-Busse, W.J.: Handling missing attribute values. In: Maimon,
O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, 2nd edn, pp. 33-51.
Springer, Berlin (2010)
17. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res.
3 , 1157-1182 (2003)
18. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction. Foundations and Appli-
cations. Springer, Berlin (2006)
19. Holland, J.H., Holyoak, K.J., Nisbett, R.E.: Induction: Processes of Inference, Learning, and
Discovery. MIT, Boston (1986)
20. Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sample performance.
IEEE Trans. Pattern Anal. Mach. Intell. 19 , 153-158 (1997)
21. Kohavi, R., John, G.: Wrappers for feature selection. Artif. Intell. 97 , 273-324 (1997)
22. Lei, Y., Huan, L.: Feature selection for high-dimensional data: a fast correlation-based filter
solution. In: Proceedings of the 20-th International Conference on Machine Learning, p. 8
(2003)
23. Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering.
IEEE Trans. Knowl. Data Eng. 17 , 491-502 (2005)
24. Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman and Hall/CRC,
Boca Raton (2007)
25. Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning
system AQ15 and its testing application on three medical domains. In: Proceedings of the
National Conference on Artificial Intelligence, pp. 1041-1045. Morgan Kaufmann, San Mateo
(1986)
26. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11 , 341-356 (1982)
27. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic
Publishers, Dordrecht (1991)
28. Pawlak, Z., Grzymala-Busse, J.W., Slowinski, R., Ziarko, W.: Rough sets. Commun. ACM 38 ,
89-95 (1995)
29. Peng, H., Fuhui, L., Chris, D.: Feature selection based on mutual information: criteria of max-
dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27 ,
1226-1238 (2005)
30. Stefanowski, J.: Algorithms of Decision Rule Induction in Data Mining. Poznan University of
Technology Press, Poznan (2001)
31. Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern
Recognit. Lett. 24 , 833-849 (2003)
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