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Fuzzy measures and coherent join measures
Author(s) -
Maturo Antonio
Publication year - 2011
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20512
Subject(s) - mathematics , additive function , join (topology) , monotonic function , probability measure , normalization (sociology) , measure (data warehouse) , extension (predicate logic) , euclidean space , euclidean geometry , consistency (knowledge bases) , point (geometry) , fuzzy logic , discrete mathematics , computer science , pure mathematics , combinatorics , data mining , artificial intelligence , geometry , mathematical analysis , sociology , anthropology , programming language
In assigning weights and scores in a decision problem usually we assume that they are finitely additive normalized measures, i.e., from the formal point of view, finitely additive probabilities. The normalization requirement sometimes appears as an actual restriction. For instance, it happens when the weights and scores can not be assigned in precise numerical terms or some logical and numerical issues arising from the given problem imply conditions that are different from the property of additivity. We must then consider extensions of the concept of probability. One, introduced by Zadeh, is to express the probabilities with fuzzy numbers; another extension, considered by Sugeno, Weber, and others, is first to replace the additivity with the condition of monotonicity, much weaker, and then to identify conditions “intermediate” between monotonicity and additivity. In any case, by assigning weights and scores, they must be consistent with the point of view considered. The conditions of consistency of finitely additive probabilities and their generalizations were discussed in several papers. This paper proposes an extension of the concept of finitely additive probability from a purely geometric point of view. Specifically, the environment of Euclidean Geometry, as de Finetti used to define the consistency of an assignment of probabilities, is replaced by the more general environment of Join Geometry by Prenowitz and Jantosciak. In this context, we introduce the concept of coherent join measure, i.e., normalized measure that is consistent with a join system, in particular, a join space or a join geometry. We show that decomposable measures with respect to a t‐conorm are special cases of join coherent measures. Finally, we present some applications, significant special cases, and possible lines of research. © 2011 Wiley Periodicals, Inc.