Learning preferences from paired opposite-based semantics

Abstract

Preference semantics examine the meaning of the preference predicate, according to the way that alternatives can be understood and organized for decision making purposes. Through opposite-based semantics, preference structures can be characterized by their paired decomposition of preference into opposite poles, and their respective valuation of binary preference relations. Extending paired semantics by fuzzy sets, preference relations can be represented in a gradual functional form, under an enhanced representational frame for examining the meaning of preference. Following a semantic argument on the character of opposition, the compound meaning of preference emerges from the fuzzy reinforcement of paired opposite concepts, searching for significant evidence for affirming dominance among the decision objects. Here we propose a general model for the paired decomposition of preference, examining its characteristic semantics under a binary and fuzzy logical frame, and identifying solutions with different values of significance for preference learning.