Sequential covering rule induction algorithm for variable consistency rough set approaches

Information Sciences

Volumn 181, Issue 5, 2011, Pages 987-1002

  Błaszczyński, Jerzy ^a   Słowiński, Roman ^a,b   Szelg, Marcin ^a  

^a POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

^b SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

Decision rule; Dominance based rough set approach; Monotonicity; Rough set; Sequential covering; Variable consistency

Indexed keywords

DECISION RULES; DOMINANCE-BASED ROUGH SET APPROACH; MONOTONICITY; ROUGH SET; SEQUENTIAL COVERING; VARIABLE CONSISTENCY;

ALGORITHMS; KNOWLEDGE BASED SYSTEMS;

ROUGH SET THEORY;

EID: 78650570828     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.10.030     Document Type: Article

References (65)

1. A. Ben-David, L. Sterling, and Y.-H. Pao Learning and classification of monotonic ordinal concepts Computational Intelligence 5 1 1989 45 49
2. A. Ben-David Monotonicity maintenance in information-theoretic machine learning algorithms Machine Learning 19 1 1995 29 43
3. A. Ben-David, and D.L. Jagerman Evaluation of the number of consistent multiattribute classification rules Engineering Applications of Artificial Intelligence 10 2 1997 205 211
4. A. Ben-David, L. Sterling, and T. Tran Adding monotonicity to learning algorithms may impair their accuracy Expert Systems with Applications 36 3 2009 6627 6634
5. J. Błaszczyński, S. Greco, and R. Słowiński Multi-criteria classification - A new scheme for application of dominance-based decision rules European Journal of Operational Research 181 3 2007 1030 1044
6. J. Błaszczyński, and R. Słowiński Incremental induction of decision rules from dominance-based rough approximations Electronic Notes in Theoretical Computer Science 82 4 2003 40 51
7. J. Błaszczyński, S. Greco, R. Słowiński, and M. Szelg On variable consistency dominance-based rough set approaches S. Greco, Y. Hata, S. Hirano, M. Inuiguchi, S. Miyamoto, H.S. Nguyen, R. Słowiński, Rough Sets and Current Trends in Computing LNAI vol. 4259 2006 Springer-Verlag Berlin 191 202 (Pubitemid 44860548)
8. J. Błaszczyński, S. Greco, R. Słowiński, and M. Szelg Monotonic variable consistency rough set approaches J. Yao, P. Lingras, W. Wu, M. Szczuka, N.J. Cercone, D. Ślzak, Rough Sets and Knowledge Technology LNAI vol. 4481 2007 Springer-Verlag Berlin 126 133
9. J. Błaszczyński, S. Greco, R. Słowiński, and M. Szelg Monotonic variable consistency rough set approaches International Journal of Approximate Reasoning 50 7 2009 979 999
10. K. Cao-Van, Supervised ranking - from semantics to algorithms, Ph.D. Thesis, Ghent University, CS Department, 2003.
11. P. Clark, and T. Niblett The cn2 induction algorithm Machine Learning 3 4 1989 261 283
12. P. Clark, and R. Boswell Rule induction with cn2: some recent improvements Y. Kodratoff, Proceedings of the Fifth European Working Session on Learning 1991 Springer-Verlag Berlin 151 163
13. W.W. Cohen Fast effective rule induction A. Priedities, S. Russell, Proceedings of the Twelfth International Conference on Machine Learning 1995 Morgan Kaufmann CA 115 123
14. W.W. Cohen, and Y. Singer A simple, fast, and effective rule learner J. Hendler, D. Subramanian, R. Uthurusamy, B. Hayes-Roth, Proceedings of the Sixteenth National Conference on Artificial Intelligence 1999 AAAI Press Menlo Park, CA 335 342
15. K. Dembczyński, R. Pindur, and R. Susmaga Generation of exhaustive set of rules within dominance-based rough set approach Electronic Notes in Theoretical Computer Science 82 4 2003 96 107
16. J. Demsar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
17. P. Fortemps, S. Greco, and R. Słowiński Multicriteria decision support using rules that represent rough-graded preference relations European Journal of Operational Research 188 1 2008 206 223
18. J.H. Friedman Recent advances in predictive (machine) learning Journal of Classification 23 2 2006 175 197
19. J.H. Friedman, and B.E. Popescu Predictive learning via rule ensembles Annals of Applied Statistics 2 3 2008 916 954
20. J. Fürnkranz Separate-and-conquer rule learning Artificial Intelligence Review 13 1999 3 54
21. J. Fürnkranz, and G. Widmer Incremental reduced error pruning W.W. Cohen, H. Hirsh, Proceedings of the 11th International Conference on Machine Learning 1994 Morgan Kaufmann CA 70 77
22. S. Greco, B. Matarazzo, and R. Słowiński The use of rough sets and fuzzy sets in MCDM T. Gal, T. Hanne, T. Stewart, Advances in Multiple Criteria Decision Making 1999 Kluwer Academic Publishers Boston 14.1 14.59 (Chapter 14)
23. S. Greco, B. Matarazzo, and R. Słowiński Rough sets theory for multicriteria decision analysis European Journal of Operational Research 129 1 2001 1 47
24. S. Greco, B. Matarazzo, R. Słowiński, and J. Stefanowski Variable consistency model of dominance-based rough sets approach W. Ziarko, Y. Yao, Rough Sets and Current Trends in Computing LNAI vol. 2005 2001 Springer-Verlag Berlin 170 181
25. S. Greco, B. Matarazzo, R. Słowiński, and J. Stefanowski An algorithm for induction of decision rules consistent with dominance principle W. Ziarko, Y. Yao, Rough Sets and Current Trends in Computing LNAI vol. 2005 2001 Springer-Verlag Berlin 304 313
26. S. Greco, B. Matarazzo, and R. Słowiński Rough approximation by dominance relations International Journal of Intelligent Systems 17 2002 153 171
27. S. Greco, B. Matarazzo, and R. Słowiński Rough sets methodology for sorting problems in presence of multiple attributes and criteria European Journal of Operational Research 138 2002 247 259
28. S. Greco, B. Matarazzo, and R. Słowiński Rough membership and bayesian confirmation measures for parameterized rough sets D. Ślzak, G. Wang, M.S. Szczuka, I. Düntsch, Y. Yao, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing LNAI vol. 3641 2005 Springer-Verlag Berlin 314 324
29. S. Greco, R. Słowiński, and Y.Y. Yao Bayesian decision theory for dominance-based rough set approach J. Yao, P. Lingras, W. Wu, M. Szczuka, N.J. Cercone, D. Ślzak, Rough Sets and Knowledge Technology LNAI vol. 4481 2007 Springer-Verlag Berlin 134 141 (Pubitemid 350270204)
30. S. Greco, B. Matarazzo, and R. Słowiński Parameterized rough set model using rough membership and Bayesian confirmation measures International Journal of Approximate Reasoning 49 2 2008 285 300
31. S. Greco, B. Matarazzo, and R. Słowiński Dominance-based rough set approach to interactive multiobjective optimization J. Branke, K. Deb, K. Miettinen, R. Słowiński, Multiobjective Optimization LNCS vol. 5252 2008 Springer Berlin 121 155
32. S. Greco, Z. Pawlak, and R. Słowiński Can Bayesian confirmation measures be useful for rough set decision rules? Engineering Applications of Artificial Intelligence 17 4 2004 345 361
33. J.W. Grzymała-Busse LERS - A system for learning from examples based on rough sets R. Słowiński, Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory 1992 Kluwer Academic Publishers Dordrecht 3 18
34. J.W. Grzymała-Busse A new version of the rule induction system LERS Fundamenta Informaticae 31 1 1997 27 39
35. J.W. Grzymała-Busse, and J. Stefanowski Three discretization methods for rule induction International Journal of Intelligent Systems 16 1 2001 29 38
36. J. Han, and M. Kamber Data Mining: Concepts and Techniques 2006 Morgan Kaufmann San Francisco, CA
37. W. Iba, J. Wogulis, and P. Langley Trading off simplicity and coverage in incremental concept learning John E. Laird, Proceedings of the 5th International Conference on Machine Learning 1988 Morgan Kaufmann CA 73 79
38. I. Kononenko, and M. Kukar Machine Learning and Data Mining 2007 Horwood Publishing, Coll House Westergate, Chichester, West Sussex
39. R.S. Michalski, On the quasi-minimal solution of the general covering problem, in: Proceedings of the 5th International Symposium on Information Processing, FCIP-69, vol. A3 (Switching Circuits), Bled, 1969, pp. 125-128.
40. R.S. Michalski A theory and methodology of inductive learning B.G. Buchanan, D.C. Wilkins, Readings in Knowledge Acquisition and Learning: Automating the Construction and Improvement of Expert Systems 1993 Morgan Kaufmann San Francisco, CA 323 348
41. R.S. Michalski, and K.A. Kaufman Data mining and knowledge discovery: a review of issues and a multistrategy approach R.S. Michalski, I. Bratko, A. Bratko, Machine Learning and Data Mining: Methods and Applications 1998 John Wiley & Sons New York, NY 71 112
42. T. Mitchell Machine Learning 1997 McGraw Hill
43. Z. Pawlak Rough sets International Journal of Information and Computer Sciences 11 1982 341 356
44. Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data 1991 Kluwer Academic Publishers Norwell, MA, USA
45. Z. Pawlak Rough sets and intelligent data analysis Information Sciences 147 1-4 2002 1 12
46. Z. Pawlak, and A. Skowron Rough membership functions R.R. Yaeger, J. Kacprzyk, M. Fedrizzi, Advances in the Dempster Shafer Theory of Evidence 1994 John Wiley & Sons New York, NY 251 271
47. Z. Pawlak, and A. Skowron Rudiments of rough sets Information Sciences 177 1 2007 3 27
48. G. Pagallo, and D. Haussler Boolean feature discovery in empirical learning Machine Learning 5 1990 71 99
49. J. Platt Machines using sequential minimal optimization B. Schoelkopf, C. Burges, A. Smola, Advances in Kernel Methods - Support Vector Learning 1998 MIT Press Cambridge, MA
50. J.R. Quinlan Learning logical definitions from relations Machine Learning 5 3 1990 239 266
51. J.R. Quinlan C4.5: Programs for Machine Learning 1992 Morgan Kaufman San Mateo, CA
52. J.R. Quinlan, and R.M. Cameron-Jones Foil: a midterm report P.B. Brazdil, Proceedings of the European Conference on Machine Learning, ECML '93 LNCS vol. 667 1993 Springer-Verlag Berlin 3 20
53. U. Ruckert, and S. Kramer A statistical approach to rule learning W.C. Cohen, A. Moore, Proceedings of the 23rd International Conference on Machine Learning, ICML'06 2006 ACM New York, NY 785 792
54. D. Ślzak Rough sets and Bayes factor Transactions on Rough Sets III 2005 202 229
55. D. Ślzak Rough sets and functional dependencies in data: foundations of association reducts Transactions on Computational Science V 2009 182 205
56. D. Ślzak, and W. Ziarko The investigation of the Bayesian rough set model International Journal of Approximate Reasoning 40 2005 81 91
57. R. Słowiński, and J. Stefanowski RoughDAS and RoughClass software implementations of rough sets approach R. Słowiński, Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory 1992 Kluwer Academic Publishers Dordrecht 445 456
58. R. Słowiński, S. Greco, and B. Matarazzo Rough set based decision support E. Burke, G. Kendall, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques 2005 Springer-Verlag New York 475 527 (Chapter 16)
59. A. Skowron Boolean reasoning for decision rules generation H.J. Komorowski, Z.W. Raś, Proceedings of Methodologies for Intelligent Systems, 7th International Symposium, ISMIS '93 1993 Springer-Verlag London 295 305
60. J. Stefanowski On rough set based approaches to induction of decision rules L. Polkowski, A. Skowron, Rough Sets in Data Mining and Knowledge Discovery vol. 1 1998 Physica-Verlag Heidelberg 500 529
61. S.M. Weiss, and N. Indurkhya Lightweight rule induction P. Langley, International Conference on Machine Learning 2000 Morgan Kaufmann CA 1135 1142
62. Y.Y. Yao Decision-theoretic rough set models J.T. Yao, P. Lingras, W.-Z. Wu, M. Szczuka, N.J. Cercone, D. Ślzak, Rough Sets and Knowledge Technology LNAI vol. 4481 2007 Springer-Verlag Berlin 11 12
63. Y.Y. Yao Probabilistic rough set approximations International Journal of Approximate Reasoning 49 2 2008 255 271
64. W. Ziarko Variable precision rough set model Journal of Computer and System Sciences 46 1 1993 39 59
65. W. Ziarko Stochastic approach to rough set theory S. Greco, Y. Hata, S. Hirano, M. Inuiguchi, S. Miyamoto, H.S. Nguyen, R. Słowiński, Rough Sets and Current Trends in Computing LNAI vol. 4259 2006 Springer-Verlag Berlin 38 48