Rule induction based on an incremental rough set

Expert Systems with Applications

Volumn 36, Issue 9, 2009, Pages 11439-11450

  Fan, Yu Neng ^a   Chern, Ching Chin ^a   Huang, Chun Che ^c  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b Bill) Tseng, Tzu Liang   (United States)

^c NATIONAL CHI NAN UNIVERSITY   (Taiwan)

Author keywords

Incremental technique; Reduct; Rough set theory; Rule induction

Indexed keywords

DYNAMIC DATABASE; EXTRACTING RULES; EXTRACTION ALGORITHMS; INCREMENTAL APPROACH; INCREMENTAL TECHNIQUE; INCREMENTAL TECHNIQUES; LARGE DATABASE; ORIGINAL ALGORITHMS; REDUCT; REDUNDANT RULES; ROUGH SET; ROUGH-SET BASED; RULE INDUCTION; RULE SET;

ALGORITHMS; DECISION TABLES; FUZZY SETS;

ROUGH SET THEORY;

EID: 67349251267     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.03.056     Document Type: Article

References (45)

1. Asharaf S., Narasimha Murty M., and Shevade S.K. Rough set based incremental clustering of interval data. Pattern Recognition Letters 27 6 (2006) 515-519
2. Bazan J.G., Peters J.F., Skowron A., Nguyen H.S., et al. Rough set approach to pattern extraction from classifiers. Electronic Notes in Theoretical Computer Science 82 4 (2003) 1-10
3. Beynon M.J., and Peel M.J. Variable precision rough set theory and data discretisation: An application to corporate failure prediction. Omega, International Journal of Management Science 29 (2001) 561-576
4. Bian X. Certain rule learning of the inconsistent data. Journal of East China Shipbuilding Institute 12 1 (1998) 25-30 (In Chinese)
5. Blaszczynski J., and Slowinski R. Incremental induction of decision rules from dominance-based rough approximations. Electronic Notes in Theoretical Computer Science 82 4 (2003) 1-12
6. Chen C.Y., Hwang S.C., and Oyang Y.J. A statistics-based approach to control the quality of subclusters in incremental gravitational clustering. Pattern Recognition 38 (2005) 2256-2269
7. Crespo F., and Weber R. A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets and Systems 150 (2005) 267-284
8. Dubois D., and Prade H. Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17 2-3 (1990) 191-209
9. Fong J., Wong H.K., and Huang S.M. Relevance feedback icon continuous and incremental data mining association rules using frame metadata model. Knowledge-Based Systems 16 (2003) 91-100
10. Grzymala-Busse J.W. LERS-a system for learning from examples based on rough sets. In: Slowinski R. (Ed). Intelligent decision support (1992), Kluwer Academic Publishers 3-18
11. Gu, X. H., Hou, D. B., Zhou, Z. K. & Yu, D. (2005). Rough set based on modified ChiMerge algorithm and its application. In Machine learning and cybernetics, proceedings of 2005 international conference, August 18-21 (Vol. 2, pp. 1004-1008).
12. Guo, S., Wang, Z. Y., Wu, Z. C. & Yan, H. P. (2005). A novel dynamic incremental rules extraction algorithm based on rough set theory. In Proceedings of the fourth international conference on machine learning and cybernetics (pp. 18-21).
13. Han J., and Kamber M. Data mining: Concepts and techniques (2001), Morgan Kaufmann Publishers, San Francisco
14. Heckerman D., Mannila H., Pregibon D., and Uthurusamy R. Proceedings of the third international conference on knowledge discovery and data mining (1997), Menlo Park, AAAI Press, CA
15. Hu, X. (1995). Knowledge discovery in databases: An attribute-oriented rough set approach. Ph.D. Thesis, Regina University.
16. Huang C.M., Hsu J.M., Lai H.Y., Huang D.T., and Pong J.C. An Estelle-based incremental protocol design system. Journal of Systems and Software 36 2 (1997) 115-135
17. Hu K.Y., Lu Y.C., and Shi C.Y. Feature ranking in rough sets. AI Communications 16 1 (2003) 41-50
18. Jan, P., Grzymala-Busse, J. W., & Zdzislaw, S. H. (2001). Melanoma prediction using data mining system LERS (COMPSAC 2001). In Proceedings of the 25th annual international computer software and applications conference, Chicago, IL, USA, October 8-12 (pp. 615-620).
19. Kalfus O., Ronen B., and Spiegler I. A selective data retention approaching massive databases. Omega, International Journal of Management Science 32 (2004) 87-95
20. Kusiak A. A feature transformation methods in data mining. IEEE Transaction on Electronics Packaging Manufacturing 24 3 (2001) 214-221
21. Law R., and Au N. Relationship modeling in tourism shopping: A decision rules induction approach. Tourism Management 21 (2000) 241-249
22. Liu, Y., Xu, C., & Pan, Y. (2004). An incremental rule extracting algorithm based on Pawlak reduction. IEEE International Conference on Systems, Man and Cybernetics (pp. 5964-5968).
23. Li R., and Wang Z.O. Mining classification rules using rough sets and neural networks. European Journal of Operational Research 157 (2004) 439-448
24. Nguyen H.S. On the decision table with maximal number of reducts. Electronic Notes in Theoretical Computer Science 82 4 (2003) 1-8
25. Pawlak Z. Rough sets. International Journal of Computer and Information Sciences 11 5 (1982) 341-356
26. Pawlak Z. Rough sets: Theoretical aspects of reasoning about data (1991), Kluwer Academic Publishers, Dordrecht
27. Richards D., and Compton P. An alternative verification and validation technique for an alternative knowledge representation and acquisition technique. Knowledge-Based Systems 12 1-2 (1999) 55-73
28. Shan N., and Ziarko W. An incremental learning algorithm for constructing decision rules. In: Kluwer R.S. (Ed). Rough sets, fuzzy sets and knowledge discovery (1994), Springer-Verlag 326-334
29. Shen Q., and Chouchoulas A. A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Engineering Applications of Artificial Intelligence 13 3 (2000) 263-278
30. Shiotani S., Fukuda T., and Shibata T. A neural network architecture for incremental learning. Neurocomputing 9 (1995) 111-130
31. Shyng J.-Y., Wang F.-K., Tzeng G.-H., and Wu K.-S. Rough set theory in analyzing the attributes of combination values for the insurance market. Expert Systems with Applications 32 1 (2007) 56-64
32. Stefanowski J. On rough set based approaches to induction of decision rules. In: Skowron A., and Polkowski L. (Eds). Rough sets in knowledge discovery Vol. 1 (1998), Physica Verlag, Heidelberg 500-529
33. Su C.-T., and Hsu J.-H. Precision parameter in the variable precision rough sets model: An application. Omega, International Journal of Management Science 34 (2006) 149-157
34. Sun, C. M., Liu, D. Y., Sun, S. Y., Li, J. F., & Zhang, Z. H. (2005). Containing order rough set methodology. In Machine learning and cybernetics, proceedings of 2005 international conference, August 18-21 (Vol. 3, pp. 1722-1727).
35. Tseng, T. L.(Bill) (1999). Quantitative approaches for information modeling. Ph.D. Dissertation, University of Iowa.
36. Tsumoto S. Extraction of experts' decision rules from clinical databases using rough set model. Intelligent Data Analysis 2 3 (1998)
37. Tsumoto S. Automated extraction of hierarchical decision rules from clinical databases using rough set model. Expert Systems with Applications 24 2 (2003) 189-197
38. Tsumoto S., and Tanaka H. PRIMEROSE: Probabilistic rule induction method based on rough sets and resampling methods. Computational Intelligence 11 (1995) 89-405
39. Wang, G. Y., Zhao, J., An, J. J., & Wu, Y. (2004). Theoretical study on attribute reduction of rough set theory: Comparison of algebra and information views. In Proceedings of the third IEEE international conference on cognitive informatics (ICCI'04).
40. Wang Q.H., and Li J.R. A rough set-based fault ranking prototype system for fault diagnosis. Engineering Applications of Artificial Intelligence 17 8 (2004) 909-917
41. Wang X., Yang J., Jensen R., and Liu X. Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Computer Methods and Programs in Biomedicine 83 (2006) 147-156
42. Ye N., and Li X. A scalable, incremental learning algorithm for classification problems. Computers and Industrial Engineering 43 (2002) 677-692
43. Zhong, N., Dong, J.-Z., Ohsuga, S., & Lin, T.-Y. (1998). An incremental, probabilistic rough set approach to rule discovery. In Fuzzy systems proceedings, IEEE world congress on computational intelligence, Anchorage, AK (pp. 933-938).
44. Zhu F., and Guan S. Relevance feedback icon ordered incremental training for GA-based classifiers. Pattern Recognition Letters 26 (2005) 2135-2151
45. Ziarko W.P. Rough sets, fuzzy sets and knowledge discovery (1994), Springer-Verlag, New York