Properties Analysis of Inconsistency-based Possibilistic Similarity Measures

Ilyes Jenhani, Salem Benferhat, Zied Elouedi.

This paper deals with the problem of measuring the similarity degree between two normalized possibility distributions encoding preferences or uncertain knowledge. Many existing definitions of possibilistic similarity indexes aggregate pairwise distances between each situation in possibility distributions. This paper goes one step further, and discusses denitions of possibilistic similarity measures that include inconsistency degrees between possibility distributions. In particular, we propose a postulate-based analysis of similarity indexes which extends the basic ones that have been recently proposed in a literature.

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