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117. S. Greco, M. Inuiguchi, and R. Slowinski. Fuzzy rough sets and multiple-premise gradual decision rules. Int. J. Approx. Reason. 41: 179 211. 2005. 118. S. Greco, M. Inuiguchi, and R. Slowinski. A new proposal for fuzzy rough approximations and gradual decision rule representation. Transactions on Rough Sets II. Lecture Notes in Computer Science, Vol. 3135. Berlin Springer, pp. 319 342. 2004. 119. J.W. Grzymala-Busse. LERS A system for learning from examples based on rough sets. In R. Slowinski, ed., Intelligent Decision Support. Dordrecht: Kluwer Academic, pp. 3 18. 1992. 120. J. W. Grzymala-Busse. A comparison of three strategies to rule induction from data with numerical attributes. Proceedings of the International Workshop on Rough Sets in Knowledge Discovery. European Joint Conferences on Theory and Practice of Software. Elsevier, pp. 132 140. 2003. 121. J. W. Grzymala-Busse. Three strategies to rule induction from data with numerical attributes. Transactions on Rough Sets II , Berlin: Springer, pp. 54 62. 2004. 122. L. Han, J.F. Peters, S. Ramanna, and R. Zhai. Classifying faults in high voltage power systems: A rough-fuzzy neural computational approach. Proceedings of 7th International Workshop on Rough Sets, Data Mining, and Granular-Soft Computing, Springer-Verlag, pp. 47 54. 1999. 123. J. Han, X. Hu, and T. Y. Lin. Feature subset selection based on relative dependency between attributes. Rough Sets and Current Trends in Computing: 4th International Conference, Uppsala, Sweden, June 1 5, pp. 176 185. 2004. 124. H. Handels, T. Ro , J. Kreusch, H. H. Wolff, and S. P pple. Feature selection for o optimized skin tumor recognition using genetic algorithms. Art. Intell. Med. 16(3): 283 297. 1999. 125. J. A. Hartigan and M. A. Wong. A k-means clustering algorithm. Appl. Stat. 28(1): 100 108. 1979. 126. I. Hayashi, T. Maeda, A. Bastian, and L.C. Jain. Generation of fuzzy decision trees by fuzzy ID3 with adjusting mechanism of AND/OR operators. In Proceedings of 7th IEEE International Conference on Fuzzy Systems. Piscataway, NJ: IEEE Press, pp. 681 685. 1998. 127. F. Herrera. Genetic fuzzy systems: Status, critical considerations and future directions. Int. J. Comput. Intell. Res. 1(1): 59 67. 2005. 128. M. P. Hippe. In J. J. Alpigini et al., eds., Towards the Classi cation of Musical Works: A Rough Set Approach. LNAI 2475, Springer Verlag, pp. 546 553. 2002. 129. S. Hirano and S. Tsumoto. Rough clustering and its application to medicine. J. Info. Sci. 124: 125 137, 2000. 130. K. Hirota, ed. Industrial Applications of Fuzzy Technology. Tokyo: Springer. 1993. 131. T. B. Ho, S. Kawasaki, and N. B. Nguyen. Documents clustering using tolerance rough set model and its application to information retrieval. Studies In Fuzziness and Soft Computing, Intelligent Exploration of the Web, Physica-Verlag Heidelberg, pp. 181 196. 2003. 132. U. H hle. Quotients with respect to similarity relations. Fuzzy Sets Sys. 27(1): 31 44. o 1988. 133. J. Holland. Adaptation In Natural and Arti cial Systems. Ann Arbor: University of Michigan Press, 1975.
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