Making decision in case-based systems using probabilities and rough sets

Knowledge-Based Systems

Volumn 16, Issue 4, 2003, Pages 205-213

  Gutiérrez Martínez, Iliana ^a   Bello Pérez, Rafael E ^a  

^a CENTRAL UNIVERSITY OF LAS VILLAS   (Cuba)

Author keywords

Case based systems; Probabilities; Rough sets; Uncertainty

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION MAKING; PROBABILITY; ROUGH SET THEORY;

CASE-BASED SYSTEMS;

KNOWLEDGE BASED SYSTEMS;

EID: 0038331668     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0950-7051(02)00036-9     Document Type: Article

References (22)

1. Kuncher K., Steimann F. Fuzzy diagnosis. Artificial Intelligence in Medicine. 16:1999;121-128.
2. Unifying expert systems and the decision sciences, Operations research, vol. 42 (3), May-June, 1994.
3. Dubois D., Godo L., Prade H., Zapico A. Making decision in a qualitative setting: from decision under uncertainty to case-based decision. Cohn A.G., Schubert L., Shapiro S.C. KR'98: Principles of Knowledge Representation and Reasoning. 1998;594-605 Morgan Kaufmann, San Francisco, CA.
4. Skapura R.M. Building Neural Networks. 1996;ACM Press, New York. p. 286.
5. Klir G., Yuan B. Fuzzy Sets and Fuzzy Logic, Theory and Applications. 1995;Prentice-Hall, Englewood Cliffs, NJ, USA.
6. Kolodner J. Case-based Reasoning. 1993;Morgan Kaufmann, San Francisco, CA.
7. Aamodt A., Plaza E. CBR: foundational issues, methodological variation and systems approach. AI Communications. 7:(1):1996.
8. Barr D.S., Mani Y.G. Using neural nets to manage investments. AI Expert. 1994.
9. Mathur K., Solow D. Operation Research: Basics Concepts in Decision Analysis. 1996;Prentice-Hall, Englewood Cliffs, NJ.
10. Klir G.J. Principles of uncertainty: what are they? Why do we need them? Fuzzy Sets and Systems. 74:1995;15-31.
11. Aamodt A., Plaza E. Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications. 7:(1):1994;39-59.
12. D. Aha, A study of instance-based algorithms for supervised learning task: mathematical empirical, a psychological observation, PhD Thesis, University of California, 1990.
13. M. Jaczynski, A framework for the management of past experiences with time extended situations, Proceedings of CIKM'97, Las Vegas, 1997.
14. Aithoff K.D. Inreca - A Seamless Integration of Induction and CBR for Decision Support Tasks. 1996;Center for Learning Systems and Applications, Department of Computer Science, University of Kaiserslantein, Germany.
15. Klir G.J., Wierman M.J. Uncertainty-based Information. 1998;Physica-Verlag, Germany.
16. Bonnisone P.P., Tong P.M. Reasoning with uncertainty in expert systems. International Journal of Man Machine Studies. 22:1985;241-250.
17. Parson S. Current approaches to handling imperfect information in data and knowledge bases. IEEE Transactions on Knowledge and Data Engineering. 8:(3):1996.
18. Shafer G. Belief functions and parametric models. Journal of Royal Statistical Society, Series B. 44:1982;322-352.
19. Zadeh L.A. Fuzzy sets as a basis for the theory of possibility. Fuzzy Sets and Systems. 1:1978;3-28.
20. Pawlak Z. Vagueness and uncertainty: a rough set perspective. Computational Intelligence. 11:(2):1995.
21. DeGroot M.H. Probability and Statistics. 1987;Addison-Wesley, Reading, MA.
22. Figueroa J.M. Guía BIP/ISO para la expresión de la incertidumbre en las mediciones. 1994;Centro Nacional de Metrología, México.