A knowledge processing method for intelligent systems based on inclusion degree

Expert Systems

Volumn 20, Issue 4, 2003, Pages 187-195

  Qiu, Guo Fang ^a   Li, Huai Zu ^a   Xu, Li Da ^a,b   Zhang, Wen Xiu ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b OLD DOMINION UNIVERSITY   (United States)

Author keywords

Databases; Inclusion degree; Intelligent systems; Knowledge discovery; Rough set

Indexed keywords

FUZZY CONTROL; INCLUSIONS; KNOWLEDGE BASED SYSTEMS; RELATIONAL DATABASE SYSTEMS; ROUGH SET THEORY; VECTORS;

INCLUSION DEGREE;

INTELLIGENT AGENTS;

EID: 0242289554     PISSN: 02664720     EISSN: None     Source Type: Journal    
DOI: 10.1111/1468-0394.00243     Document Type: Article

References (21)

1. ARROW, K. and H. RAYNAUD (1986) Social Choice and Multcriterion Decision-Making, Cambridge, MA: MIT Press.
2. BIRKHOFF, G. (1967) Lattice Theory, Providence, RI: American Mathematical Society.
3. FENG, S. and L. Xu (1999) Decision support for fuzzy comprehensive evaluation of urban development, Fuzzy Sets and Systems, 105 (1), 1-12.
4. GUAN, J. and D. BELL (1998) Rough computational methods for information systems, Artificial Intelligence, 105, 77-103.
5. HU, G. (1992) Information and probable inference, in Automated Reasoning: Proceedings of an International Workshop on Automated Reasoning, Z. Shi (ed.), Amsterdam, North Holland, 84-107.
6. KRUSE, R., E. SCHWECKE and J. HEINSOHN (1991) Uncertainty and Vagueness in Knowledge Based Systems, Berlin: Springer.
7. LEUNG, K. and M. WANG (1992) Fuzzy concepts in an object-oriented expert system, International Journal of Intelligent Systems, 7 (2), 171-192.
8. MA, Z., W. ZHANG and W. MA (1999) Assessment of data redundancy in fuzzy relational databases based on semantic inclusion degree, Information Processing Letters, 72, 25-29.
9. MOORE, R. (1979) Methods and Applications of Interval Analysis, Philadelphia, PA: SIAM.
10. NEAPOLITAN, R. (1990) Probabilistic Reasoning in Expert Systems: Theory and Algorithms, New York: Wiley.
11. PAWLAK, Z. (1982) Rough sets, International Journal of Computer and Information Sciences, 11, 341-356.
12. PAWLAK, Z. (1991) Rough Sets: Theoretical Aspects of Reasoning about Data, Boston, MA: Kluwer Academic.
13. PEARL, J. (1988) Probabilistic Reasoning in Intelligent Systems, San Francisco, CA: Morgan Kaufmann.
14. POLYA, G. (1954) Patterns of Plausible Inference, Princeton, NJ: Princeton University Press.
15. SHAFER, G. (1976) A Mathematical Theory of Evidence, Princeton, NJ: Princeton University Press.
16. WU, W., Y. LEUNG and W. ZHANG (2002) Connections between rough and Dempster-Shafer theory of evidence, International Journal of General Systems, 31 (4), 405-430.
17. XU, Z., J. LIANG, C. DANG and K. CHIN (2002) Inclusion degree: a perspective on measures for rough set data analysis, Information Sciences, 141, 227-236.
18. ZADEH, L. (1965) Fuzzy sets, Information and Control, 8, 338-353.
19. ZADEH, L. and J. KACPRZYH (1992) Fuzzy Logic for the Management of Uncertainty, New York: Wiley.
20. ZHANG, W. and Y. LEUNG (1996a) The Uncertainty Reasoning Principles, Xian: Xian Jiaotong University Press.
21. ZHANG, W. and Y. LEUNG (1996b) Theory of including degrees and its applications to uncertainty inferences, Soft Computing in Intelligent Systems and Information Processing, New York: IEEE, 496-501.