1. Clark P, Niblett T. The CN2 algorithm. Machine Learning, 1989, 3(4): 261-283
2. Kaufman K A, Michalski R S. Learning in an inconsistent world: rule selection in AQ18. Reports of the machine learning and inference laboratory, MLI99-1, Fairfax, VA, USA: George Mason University, 1999
3. Joachims T. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. Proceedings of the 14th International Conference on Machine Learning (ICML-97), Jul 8-12, 1997, Nashville, TN, USA. San Francisco, CA, USA: Morgan Kaufmann, 1997: 143-151
4. Quinlan J R. C4.5: programs for machine learning. San Francisco, CA, USA: Morgan Kaufmann, 1993
5. Dougherty J, Kohavi R, Sahami M. Supervised and unsupervised discretization of continuous features. Proceedings of the 12th International Conference on Machine Learning (ICML-95), Jul 9-12, 1995, Tahoe City, CA, USA. San Francisco, CA, USA: Morgan Kaufmann, 1995: 194-202
6. Liu H, Hussain F, Tan C L, et al. Discretization: an enabling technique. Data Mining and Knowledge Discovery, 2002, 6(4): 393-423
7. Pawlak Z, Grzymala B J, Slowinski R, et al. Rough sets. Communications of the ACM, 1995, 38(11): 89-95
8. Blajdo P, Grzymala B J, Zdzislaw S H, et al. A comparison of six approaches to discretization-A rough set perspective. Proceedings of the 3rd International Conference on Rough Sets and Knowledge Technology (RSKT’08), May 17-19, 2008, Chengdu, China. LNCS 5009. Berlin, Germany: Springer-Verlag, 2008: 31-38
9. Nguyen S H, Skowron A. Quantization of real value attributes: rough set and boolean reasoning approach. Bulletin of International Rough Set Society, 1996, 1(1): 5-16
10. Dai J H, Li Y X. Study on discretization based on rough set theory. Proceedings of the 1st International Conference on Machine Learning and Cybernetics, Nov 4-5, 2002, Beijing, China. Beijing, China: Publishing House of Electronics Industry, 2002: 1371-1373
11. Pan W, Li J C, Wang Y S, et al. A new algorithm of discretization of consecutive attribute based on the decision in rough sets. Computer Science, 2007, 34(8): 208-210 (in Chinese)
12. Fomina M, Kulikov A, Vagin V. The development of the generalization algorithm based on the rough set theory. Information Theories and Applications, 2005, 13(4): 255-262
13. Nguyen S H. Discretization problems for rough set methods. Proceedings of the 1st International Conference on Rough Sets and Current Trend in Computing (RSCTC’98), Jun 22-26, 1998, Warsaw, Poland. LNCS 1424. Berlin, Germany: Springer-Verlag, 1998: 545-552
14. Hou L J, Wang G Y, Nie N, et al. Discretization in rough set theory. Computer Science, 2000, 27(12): 89-94 (in Chinese)
15. Ning W, Zhao M Q. More improved greedy algorithm for discretizaflon of decision table. Computer Engineering and Applications, 2007, 43(3): 173-174, 178 (in Chinese)
16. Hou L J, Yan H W. Discretization algorithm in rough set based on binary discernibility matrix’s tansformation. Computer Engineering and Design, 2008, 29(9): 2330-2332 (in Chinese)
17. Zhao J, Wang G Y, Wu Z F, et al. New algorithms for data discretization based on rough set theory. Journal of Chongqing University: Natural Science, 2002, 25(3): 18-21 (in Chinese)
18. Chang L Y, Wang G Y, Wu Y, et al. An approach for attribute reduction and rule generation based on rough set theory. Journal of Software, 1999, 10(11): 1206-1211 (in Chinese)
19. Mollestad T, Skowron A. Rough set framework for data mining of propositional default rules. Proceedings of the 9th International Symposium on Methodologies for Intelligent Systems (ISMIS’96), Jun 9-13, 1996, Zakopane, Poland. LNCS 1079. Berlin, Germany: Springer-Verlag, 1996: 448-457 |