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Tytuł artykułu

Disset: a disjunctive sorting method based on evidence theory

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We consider sorting problems where information about categories is represented by a Learning Set (LS), i.e. a set of alternatives and their related labels. The distinctive feature of our approach relies on the fact that both precise and imprecise information about the LS can be handled. More precisely, we assume that each alternative of the LS may belong to a unique category or a disjunction of successive categories. Our method proceeds in four stages: the comparison, the definition of Basic Belief Assignments (BBA's), the combination and the assignment. Artificial data sets are used to test the method and to compare its results with those provided by an ELECTRE TRI like procedure.
Słowa kluczowe
Rocznik
Strony
253--274
Opis fizyczny
Bibliogr. 15 poz.
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autor
autor
Bibliografia
  • [1] Appriou A., Probabilites et Incertitude en fusion de donnees multisenseurs, Revue Scientifique et Technique de la Defense, 11. 1991,27-40.
  • [2] Bogler P.L., Shafer-Dempster reasoning with applications to multisensor target identification systems, IEEE Transactions on Systems. Man and Cybernetics. 17. 1987, 968-977.
  • [3] Brans J.P., Vincke Ph., A preference ranking organisation method: the PROMETHEE method. Management Science. 31,1985, 647-656.
  • [4] Denoeux T., A k-nearest neighbour classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems Man and Cybernetics, 25, 1995, 804-813.
  • [5] Roy B., Bouyssou D., Aide multicritere a la decision: melhodes et cas, Economica, Paris, 1993.
  • [6] Sentz K., Person S., Combination of evidence in Dempster-Shafer theory', 6lh World Multi-conference on Systemics: Cybernetics and Informatics, 2002.
  • [7] Shafer G., A mathematical theory of evidence, Princeton University Press. Princeton and London, 1976.
  • [8] Shafer G., Belief functions and parametric models, Royal Statistical Society, 3. 1982, 322-352.
  • [9] Smets Ph., Belief functions. Smets Ph., Mamdani A., Dubois D., Prade H. (eds), N on Standard Logics for Automated Reasoning, Academic Press, London, 1988. 253-286.
  • [l0] Srnets Ph., Decision making in a context where uncertainty is represented by belief functions. Srivastava R., Mock T.J. (eds). Belief Functions in Business Decisions.Physica-Verlag, Heidelberg, Germany, 2002, 17-61.
  • [11] Vincke Ph., L'Aide Multicritere a la Decision, Editions de 1'Universite de Bruxelles, Editions Ellipses, Bruxelles, 1989.
  • [12] Y. Wei, ELECTRE TRI: Aspects methodologiques et manual d'utilisation. Document du Lamsade, 74, 1992.
  • [13] Zopounidis C., Doumpos M., Business failure prediction using the UTAD1S multicriteria analysis method, The Journal of the Operational Research Society-. 50, 1999, 1138-1148.
  • [14] Zopounidis C., Doumpos M., Multicriteria Classification and Sorting Methods: A Literature Review, European Journal of Operational Research, 13 8, 2002, 229-246.
  • [15]Zopounidis C., MCDA methodologies for classification and sorting, European Journal of Operational Research. 1387 2002, 227-228.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP1-0081-0049
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