A two-phase model for learning rules from incomplete data

Fundamenta Informaticae

Volumn 94, Issue 2, 2009, Pages 219-232

  Li, Huaxiong ^a   Yao, Yiyu ^b   Zhou, Xianzhong ^a   Huang, Bing ^c  

^a NANJING UNIVERSITY   (China)

^b UNIVERSITY OF REGINA   (Canada)

^c NANJING AUDIT UNIVERSITY   (China)

Author keywords

Filled in values; Missing attribute values; Two phase rule induction

Indexed keywords

ATTRIBUTE VALUES; FILLED-IN VALUES; FILLING IN; INCOMPLETE DATA; LEARNING RULES; LEARNING STRATEGY; MISSING ATTRIBUTE VALUES; MISSING VALUES; NEW FORMS; PHASE RULES; PRACTICAL SOLUTIONS; RULE INDUCTION; SECOND PHASE; TWO PHASIS; TWO-PHASE MODEL; TWO-PHASE RULE INDUCTION;

EID: 70349107625     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: 10.3233/FI-2009-127     Document Type: Article

References (23)

1. Blake C. L., Merz C. J.: UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
2. Cendrowska J.: PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies, 27(4), 1987, 349-370.
3. Clark P., Niblett T.: The CN2 induction algorithm. Machine Learning, 3(4), 1989, 261-283.
4. Ghahramani, Z., Jordan, M. I.: Supervised learning from incomplete data via an EM approach, in: Advances in Neural Information Processing Systems 6 (J. D. Cowan, G. Tesauro, J. Alspector, Eds.), San Mateo, CA: Morgan Kaufman, 1994, 120-127.
5. Greco S., Matarazzo B., Slowinski R.: Handling missing values in rough set analysis of multi-attribute and multi-criteria decision problems. Proc. RSFDGrC'99, 7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing (N. Zhong, A. Skowron, S. Ohsuga, Eds.), LNAI 1711, Springer- Verlag, Berlin Heidelberg, 1999, 146-157.
6. Grzymala-Busse, J. W.: On the unknown attribute values in learning from examples. Proc. ISMIS'91, 6th International Symposium on Methodologies for Intelligent Systems (Z. Ras, M. Zemankova, Eds.), LNAI 542, Springer-Verlag, Berlin, 1991, 368-377.
7. Grzymala-Busse J. W. and Hu M.: A comparison of several approches to missing attribute values in data mining. Proc. RSCTC'00, 2nd International Conference on Rough Sets and Current Trends in Computing( W. Ziarko, Y. Yao, Eds.), LNAI 2005, Springer, Berlin, 2000, 378-385.
8. Grzymala-Busse J. W. and Grzymala-BusseW. J.: An experimental comparison of three rough set approaches to missing attribute values. LNCS Transactions on Rough Sets VI, LNCS 4374, Springer, Berlin, 2007, 31-50.
9. Grzymala-Busse J.W.: Data with missing attribute values: Generalization of indiscernibility relation and rule induction. LNCS Transactions on Rough Sets I, LNCS 3100, Springer, Berlin, 2004, 78-95.
10. Kryszkiewicz M.: Rough Set Approach to Incomplete Information Systems. Information Sciences, 112, 1998, 39-49.
11. Kryszkiewicz M.: Rules in Incomplete Information Systems. Information Sciences, 113, 1999, 271-292.
12. Laurikkala J., Kentala E., Juhola M., et al.: Usefulness of imputation for the analysis of incomplete otoneurologic data. International Journal of Medical Informatics, (58-59), 2000, 235-242.
13. Li H. X., Yao Y. Y., Zhou X. Z., Huang B.Two-phase rule induction from incomplete dataProc. RSKT'08 (G. Wang et al., Eds.), LNAI 5009, Springer-Verlag, Berlin Heidelberg, 47-54.
14. Mitchell T.M.: Generalization as Search, Artificial Intelligence, 18, 1982, 203-226.
15. Mitchell T.M.: Machine Learning, McGraw-Hill Higher Education(1997)
16. Polkowski L., Artiemjew P.: Granular classifiers and missing values, Proc. ICCI'07, 2007, 186-194.
17. Quinlan J. R.: C4.5: Programs for machine learning.Morgan Kaufmann, San Mateo, CA (1993)
18. Stefamowski J., Tsoukeas A.: On the extension of rough sets under incomplete information. International Journal of Intelligent System, 16, 1999, 29-38.
19. Yao J. T., Yao Y. Y.: Induction of classification rules by granular computing. Proceedings of RSCTC'02, LNAI 2475, 2002, 331C338.
20. Yao Y. Y.: Concept formation and learning: A cognitive informatics perspective, Proceedings of the ICCI'04, 2004, 42-51.
21. Yao Y. Y., Zhong N.: An analysis of quantitativemeasures associated with rules. Proceedings of PAKDD'98, LNAI 1974, Springer, Berlin, 1999, 479-488.
22. Zhang S. C., Qin Z. X., Ling C. X., Sheng S. L.: Missing is Useful": Missing values in cost-sensitive decision trees. IEEE Transactions on Knowledge and Data Engineering, 17, 2005, 1689-1693.
23. Zhao, Y., Yao, Y. Y., Yao, J. T.: Level-wise construction of decision trees for classification. International Journal of Software Engineering and Knowledge Engineering, 16(1), World Scientific, 2006, 103-123.