Advertisement

The Approach to Multicriteria Decision Making Based on Pattern Recognition Algorithms

  • Anna Perekhod
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 465)

Abstract

The paper introduces Multiple Criteria Decision Making Problems with Incomplete Information (MCDMII). Usually such problems arise in Intelligent Decision Support Systems (IDSS) development or other cases of decision making practical realization. In most of cases, any practical problem is multicriteria, because a compromise between estimations by different criteria is necessary for the best solution choice [1]. On the other hand, it is a problem with incomplete information, because, as a rule, an exact setting of all data components in the decision making model is impossible [2].

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Larichev, O.: Objective models and subjective solutions. Nauka, Moscow. 1987.Google Scholar
  2. 2.
    Donskoy, V., Bashta, A.: Discrete models for decision making with incomplete information. Tavria, Simferopol. 1992Google Scholar
  3. 3.
    Donskoy, V., Perekhod, A.: On the multicriteria optimization problem with incomplete information about criteria. Abstracts of the International Conference in Multi-Objective Programming, Malaga, Spain, May 16–18, 1996, pp. 52–53Google Scholar
  4. 4.
    Perekhod, A.: Multicriteria decision making models with incomplete information for intelligent systems. Abstracts of the International Conference on Intelligent Data Processing, Alushta, Ukraine, June 3–7, 1996, pp. 27–28Google Scholar
  5. 5.
    Zakrevski, A.: Logic of the recognition. Nauka, Minsk. 1988Google Scholar
  6. 6.
    Donskoy, V.I.: Decision trees learning algorithms. Zh. Vychisl. Math. Math. Fiz. 22, 4 (1982) 963–974Google Scholar
  7. 7.
    Donskoy, V.I.: Weakly defined problems of Boolean linear programming with a partially specified set of admissible solutions. Zh. Vychisl. Math. Math. Fiz. 28, 9 (1988) 1379–1385Google Scholar
  8. 8.
    Tou, J., Gonzalez, R.: Pattern recognition principles. Addison-Wesley Publishing Company Inc., Advanced Book Program Reading, Massachusetts, London, etc. 1974Google Scholar
  9. 9.
    Orlov, V.: Graph-schemes of recognition algorithms. Nauka, Moscow. 1982Google Scholar
  10. 10.
    Perekhod, A.: The algorithm for Pareto sets approximation on multigraphs. Programs, Systems, Models. (1996) 18–26Google Scholar
  11. 11.
    Gamkrelidze, L. Ch., Ostroukh, E.N.: Methods for a solving of the discrete multicriteria optimization problems. Izv. Ross. Acad. Nauk, Tech. Cybem. 3 (1989) 150–155Google Scholar
  12. 12.
    Langley, P., Simon, H.: Application of Machine Learning and Rule Induction. Communications of the ACM. 38,11 (1995) 55–64CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Anna Perekhod
    • 1
  1. 1.Department of MathematicsSimferopol State UniversitySimferopolUkraine

Personalised recommendations