Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions

• Published in: Applied soft computing 2018-06, Vol.67, p.641-651
• Main Authors: Franco, Camilo, Hougaard, Jens Leth, Nielsen, Kurt
• Format: Article
• Language: English
• Subjects: Decision attitudes; Decision support systems; Imprecision; Interval degrees; Robust preferences; Semi-equivalence; Weighted overlap dominance
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.07.031

[Display omitted] •Weighted overlap dominance is formalized for a general class of multi-criteria problems.•The WOD-relevance mechanism allows learning preferences and attitudes from uncertain data.•Preferences are robust with respect to a set of (inferred) decision attitudes.•The moderation of the WOD system allows optimal/robust decision making. This paper proposes an interval-valued multi-criteria method for learning preferences and attitudes, identifying priorities with maximal robustness for decision support. The method is based on the notion of weighted overlap dominance, formalized by means of aggregation operators and interval-valued fuzzy sets. The procedure handles uncertainty by estimating the likelihood of dominance among pairs of alternatives, inducing an attitude-based system of dominance and indifference relations. This system allows conflicting situations of indifference/dependency to arise, which need to be resolved for properly identifying preferences under any attitude. In order to do so, relevance functions are examined over the whole system of relations, obtaining a weak preference order together with its associated attitude and robustness index. As a result, the proposed method allows learning preferences and attitudes, identifying the solutions with maximal robustness for intelligent decision support.