Weighted fuzzy pattern matching

Abstract

The fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared in a matching process. This technique has proved very useful for implementing patterns of approximate reasoning in expert system inference engines, and for designing retrieval systems capable of managing incomplete and fuzzy information data bases and vague queries. In this paper, the fuzzy pattern matching procedure is improved by introducing weights assessing the relative importance of atoms in the pattern. Matching indices are obtained using weighted versions of the minimum and maximum operations of fuzzy set theory. This approach is extended to the case of variable weights, and is related to aggregation schemes proposed by Yager when modeling soft quantified statements such as "most of the criteria must be met". A notion of soft partial matching is thus modelled. © 1988.