A fuzzy-set-theoretic feature model and its application to asymmetric similarity data analysis

Abstract

Argues that feature representation models are too restricted because they consider features as dichotomous variables. To construct a more general feature model, it is shown that fuzzy set theory (L. A. Zader, 1965) gives a natural solution to the problem. Using fuzzy set theory in place of ordinary set theory, a fuzzy feature matching model, which is a generalization of A. Tversky's (1977) contrast model of similarity, is proposed and applied to the analysis of an asymmetric similarity matrix obtained by asking 42 undergraduates to judge pairwise similarity among 8 countries. (PsycINFO Database Record (c) 2012 APA, all rights reserved)