A new method for constructing fuzzy decision trees and generating fuzzy classification rules from training examples

Cybernetics and Systems

Volumn 31, Issue 7, 2000, Pages 763-785

  Chen, Shyi Ming ^a   Lin, Shih Yirng ^b  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUZZY SETS; ITERATIVE METHODS; MEMBERSHIP FUNCTIONS; TREES (MATHEMATICS);

COMPOUND ANALYSIS TECHNIQUES; FUZZY CLASSIFICATION RULES; FUZZY DECISION TREES;

DECISION THEORY;

EID: 0034309375     PISSN: 01969722     EISSN: 10876553     Source Type: Journal    
DOI: 10.1080/01969720050192054     Document Type: Article

References (20)

1. Boyen, X., and L. Wehenkel. 1999. Automatic induction of fuzzy decision trees and its application to power system security assessment. Fuzzy Sets Syst. 102(1):3-19.
2. Chang, R. L. P., and T. Pavlidis. 1977. Fuzzy decision tree algorithms. IEEE Trans. Syst., Man, Cybern 7(1):28-35.
3. Chen, S. M. 1988. A new approach to handling fuzzy decision making problems. IEEE Trans. Syst., Man, Cybern. 18(6):1012-1016.
4. Chen, S. M., and M. S. Yeh. 1997. Generating fuzzy rules from relational database systems for estimating null values. Cybernetics and Systems: An International Journal 28(8):695-723.
5. Hart, A. 1995. The role of induction in knowledge elicitation, Expert Systems 2(1):24-28.
6. Hunt, E. B., J. Marin, and P. J. Stone. 1966. Experience in induction. New York: Academic Press.
7. Jeng, B., and T. P. Liang. 1993. Fuzzy indexing and retrieval in case-based systems. In Proc. 1993 Pan Pacific Conference on Information Systems, Taiwan, R. O. C., 258-266.
8. Lin, S. Y., and S. M. Chen. 1996. A new method for generating fuzzy rules from fuzzy decision trees. In Proc. 7th International Conference on Information Management, Chungli, Taoyuan, Taiwan, R. O. C., 358-364.
9. Mingers, J. 1989. An empirical comparison of pruning methods for decision tree induction. Machine Learning 4(2):227-243.
10. Quinlan, J. R. 1979. Discovering rules by induction from large collection of examples. In Expert systems in the micro electronic age, D. Michie, ed. Edinburgh: Edinburgh University Press.
11. Quinlan, J. R. 1986. Introduction of decision trees. Machine Learning 1(1):81-106.
12. Quinlan, J. R. 1990. Decision trees and decisionmaking. IEEE Trans. Syst., Man, Cybern. 20(2):339-346.
13. Safavian, S. R., and D. Landgrebe. 1991. A survey of decision tree classiher methodology. IEEE Trans. Syst., Man, Cybern. 21(3):660-674.
14. Sun, S. W. 1995. A fuzzy approach to decision trees. M.S. Thesis, Institute of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan, R. O. C.
15. Wu, T. P., and S. M. Chen. 1999. A new method for constructing membership functions and fuzzy rules from training examples. IEEE Trans. Syst., Man, Cybern.-Part B 29(1):25-40.
16. Yasdi, R. 1991. Learning classihcation rules from database in the context of knowledge acquisition and representation. IEEE Trans. Syst., Man, Cybern. 3(3):293-306.
17. Yeh, M. S., and S. M. Chen. 1995. An algorithm for generating fuzzy rules from relational database systems. In Proc. 6th International Conference on Information Management, Taipei, Taiwan, R. O. C., 219-226.
18. Yuan, Y., and M. J. Shaw. 1995. Induction of fuzzy decision trees. Fuzzy Sets Syst. 69(2):125-139.
19. Zadeh, L. A. 1965. Fuzzy sets. Inf. Contr. 8:338-353.
20. Zadeh, L. A. 1975. The concepts of a linguistic variable and its application to approximate reasoning (I). Information Sciences 8:199-249.