Rough set learning of preferential attitude in multi-criteria decision making

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 689 LNAI, Issue , 1993, Pages 642-651

  Slowiński, Roman ^a  

^a POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; DECISION SUPPORT SYSTEMS; DECISION THEORY; INTELLIGENT SYSTEMS;

DECISION MAKERS; DECISION POLICY; DECISION SITUATION; DECISION SUPPORTS; DISTANCE MEASURE; MULTI CRITERIA DECISION MAKING; MULTI-CRITERIA; NEW CASE;

ROUGH SET THEORY;

EID: 17644423877     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-56804-2_60     Document Type: Conference Paper

References (16)

1. Fibak, J., Pawlak, Z., Slowinski, K., Slowinski, R. (1986). Rough sets based decision algorithm for treatment of duodenal ulcer by HSV. Bulletin o] the Polish Academy of Sciences, set. Biological Sciences, 34, 227-246.
2. Grzymala-Busse, J.W. (1992). LERS - a system for learning from examples based on rough sets. In: Slowinski, ed. (1992), pp. 3-18.
3. Krysinski, J. (1990). Rough sets approach to the analysis of the structure-activity relationship of quaternary imidazolium compounds. Arzneimittel-Forschung/Drug Research 40 (II), 795-799.
4. Michalski, R.S. (1983). A theory and methodology of inductive learning. In: Machine Learning (R.S. Michalski, J.G. Carbonell, T.M. Mitchell, eds.), Morgan Kaufmann, pp. 83-134.
5. Moscarola, J. (1978). Multicriteria decision aid - two applications in education management. In: Multiple Criteria Problem Solving (S. Zionts, ed.), Lecture Notes in Economics and Mathematical Systems, vo1. 155, Springer-Verlag, Berlin, pp. 402-423.
6. Nowicki, R., Slowinski, R., Stefanowski, J. (1992). Rough sets analysis of diagnostic capacity of vibroacoustic symptoms. Journal of Computers & Mathematics with Applications 24, 109-123.
7. Pawlak, Z. (1982). Rough Sets. International Journal of Information & Computer Sciences 11, 341-356.
8. Pawlak, Z. (1991). Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht/Boston/London.
9. Roubens, M., Vincke, Ph. (1985). Preference Modelling. Lecture Notes in Economics and Mathematical Systems, vol. 250, Springer-Verlag, Berlin.
10. Roy, B. (1985). Méthodologie Multicritère d’Aide à la Décision. Economica, Paris.
11. Roy, B. (1989). Main Source of inaccuurate determination, uncertainty and imprecision in decision models. Math. Comput. Mode, 12. 1245-1254.
12. Roy, B. (1992). Decision science or decision aid science. European Journal of Operational Research, Special Issue on Model Validation in Operations Research (to appear).
13. Slowinski, R., ed. (1992). Intelligent Decision Support. Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht/Boston/London.
14. Slowinski, K., Slowinski, R., Stefanowski, J. (1988). Rough sets approach to analysis of data from peritoneal lavage in acute pancreatitis. Medical Informatics 13, 143-159.
15. Slowinski, R., Stefancwski, J. (1992). ‘RoughDAS’ and ‘RoughClass’ software implementations of the rough sets approach. In: Slowinski, ed. (1992), pp. 445-456.
16. Stefanowski, J. (1992). Classification support based on the rough sets theory. Proc. IIASA Workshop on User-Oriented Methodology and Techniques of Decision Analysis and Support, Serock, Sept. 9-13, 1991 (to be published by Springer-Verlag).