A function-decomposition method for development of hierarchical multi-attribute decision models

Decision Support Systems

Volumn 36, Issue 3, 2004, Pages 215-233

  Bohanec, Marko ^a,b   Zupan, Blaž ^b,c  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

^c BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

Data mining; Data driven modeling; Function decomposition; Hierarchical models; Multi attribute decision making

Indexed keywords

DATA MINING; DECISION MAKING; HIERARCHICAL SYSTEMS; HUMAN COMPUTER INTERACTION; LEARNING SYSTEMS;

DATA DRIVEN MODELING; FUNCTION DECOMPOSITION; HIERARCHICAL MODELS;

DECISION SUPPORT SYSTEMS;

EID: 0344444368     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9236(02)00148-3     Document Type: Article

References (33)

1. R.L. Ashenhurst, The Decomposition of Switching Functions, Technical report, Bell Laboratories BL-1 (11) (1952) 541-602.
2. Bohanec M., Rajkovič V. DEX: an expert system shell for decision support. Sistemica. 1(1):1990;145-157.
3. Bohanec M., Cestnik B., Rajkovič V. A management decision support system for allocating housing loans. Humpreys P., Bannon L., McCosh A., Migliarese P. Implementing System for Supporting Management Decisions. 1996;34-43 Chapman & Hall, London.
4. Bohanec M., Zupan B., Rajkovič V. Applications of qualitative multi-attribute decision models in health care. International Journal of Medical Informatics. 58-59:2000;191-205.
5. Bose U., Davey A.M., Olson D.L. Multi-attribute utility methods in group decision making: past applications and potential for inclusion in GDSS. Omega. 25(6):1997;691-706.
6. Clemen R.T. Making Hard Decisions: An Introduction to Decision Analysis. 1996;Duxbury Press, Belmont.
7. Curtis H.A. A New Approach to the Design of Switching Functions. 1962;Van Nostrand, Princeton.
8. Decision Analysis Society, MAU and AHP software, WWW page available at: http://www.faculty.fuqua.duke.edu/daweb/dasw1.htm (2002).
9. Demšar J., Zupan B., Bohanec M., Bratko I. Constructing intermediate concepts by decomposition of real functions. van Someren M., Widmer G. Machine Learning: ECML-97. 1997;93-107 Springer-Verlag, Berlin.
10. Han J., Kamber M. Data Mining: Concepts and Techniques. 2001;Morgan Kaufman Publishers, San Francisco.
11. Keeney R.L., Raiffa H. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. 1976;Wiley, New York.
12. Luba T. Decomposition of multiple-valued functions. 25th Intl. Symposium on Multiple-Valued Logic (Bloomigton, IN):1995;256-261.
13. Mallach E.G. Understanding Decision Support Systems and Expert Systems. 1994;Irwin, Boston.
14. Michie D. Problem decomposition and the learning of skills. Lavrač N., Wrobel S. Machine Learning: ECML-95, Notes in Artificial Intelligence 912. 1995;17-31 Springer-Verlag, Berlin.
15. Michie D., Spiegelhalter D.J., Taylor C.C. Machine Learning, Neural and Statistical Classification. 1994;Ellis Horwood, London. [http://www.amsta.leeds.ac.uk/̃charles/statlog/].
16. A. Perkowski, T. Luba, S. Grygiel, P. Burkey, M. Burns, N. Iliev, M. Kolsteren, R. Lisanke, R. Malvi, Z. Wang, H. Wu, F. Yang, S. Zhou, J.S. Zhang, Unified Approach to Functional Decompositions of Switching Functions. Technical report, Warsaw University of Technology and Eindhoven University of Technology, 1995.
17. Phillips L.D. Decision analysis and its applications in industry. Mitra G. Computer Assisted Decision Making. 1986;189-197 North-Holland, Amsterdam.
18. Quinlan J.R. C4.5: Programs for Machine Learning. 1993;Morgan Kaufman Publishers, San Mateo.
19. Rajkovič V., Bohanec M. Decision support by knowledge explanation. Sol H.G., Vecsenyi J. Environments for Supporting Decision Processes. 1991;47-57 North Holland, Amsterdam.
20. Rajkovič V., Delidžakova-Drenik E., Urh B. Comparative analysis of three tools for the development and exploitation of expert system knowledge base (in Slovene). Informatica. 15(3):1991;22-28.
21. Ross T.D., Noviskey M.J., Gadd D.A., Goldman J.A. Pattern theoretic feature extraction and constructive induction. Proc. ML-COLT '94 Workshop on Constructive Induction and Change of Representation (New Brunswick, NJ):1994.
22. Saaty T.L. Multicriteria Decision Making: The Analytic Hierarchy Process. 1993;RWS Publications, Pittsburgh.
23. Salzberg S.L. On comparing classifiers: pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery. 1:1997;317-328.
24. Samuel A. Some studies in machine learning using the game of checkers II: recent progress. IBM Journal of Research and Development. 11:1967;601-617.
25. A.D. Shapiro, Structured Induction in Expert Systems, Turing Institute Press in association with Addison-Wesley Publishing Company, Wokingham, 1987.
26. Shaw M.J. Machine learning methods for intelligent decision support: an introduction. Decision Support Systems. 10(2):1993;79-83.
27. Shaw M.J., Subramaniam C., Tan G.W., Welge M.E. Knowledge management and data mining for marketing. Decision Support Systems. 31:2001;127-137.
28. Stewart T.J. A critical survey on the status of multiple criteria decision making theory and practice. Omega. 20(5/6):1992;569-586.
29. Wan W., Perkowski M.A. A new approach to the decomposition of incompletely specified functions based on graph-coloring and local transformations and its application to FPGA mapping. Proceedings IEEE EURO-DAC-92. 1992;230-235. Hamburg.
30. Zanakis S.H., Solomon A., Wishart N., Dublish S. Multi-attribute decision making: a simulation comparison of select methods. European Journal of Operational Research. 107:1998;507-529.
31. B. Zupan, Machine Learning Based on Function Decomposition, PhD Thesis, University of Ljubljana, Faculty of Computer and Information Sciences, 1997.
32. Zupan B., Bohanec M., Demšar J., Bratko I. Learning by discovering concept hierarchies. Artificial Intelligence. 109:1999;211-242.
33. Zupan B., Bratko I., Bohanec M., Demšar J. Induction of concept hierarchies from noisy data. Langley P. Proc. International Conference on Machine Learning ICML-2000. 2000;1199-1206 Morgan Kaufman Publishers, San Francisco.