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Partial Ranking by Incomplete Pairwise Comparisons Using Preference Subsets

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Belief Functions: Theory and Applications (BELIEF 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8764))

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Abstract

In multi-criteria decision making the decision maker need to assign weights to criteria for evaluation of alternatives, but decision makers usually find it difficult to assign precise weights to several criteria. On the other hand, decision makers may readily provide a number of preferences regarding the relative importance between two disjoint subsets of criteria. We extend a procedure by L. V. Utkin for ranking alternatives based on decision makers’ preferences. With this new method we may evaluate and rank partial sequences of preferences between two subsets of criteria. To achieve this ranking it is necessary to model the information value of an incomplete sequence of preferences and compare this with the belief-plausibility of that sequence in order to find the partial ranking of preferences with maximum utility.

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Schubert, J. (2014). Partial Ranking by Incomplete Pairwise Comparisons Using Preference Subsets. In: Cuzzolin, F. (eds) Belief Functions: Theory and Applications. BELIEF 2014. Lecture Notes in Computer Science(), vol 8764. Springer, Cham. https://doi.org/10.1007/978-3-319-11191-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-11191-9_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11190-2

  • Online ISBN: 978-3-319-11191-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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