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ABSTRACT: Several existing similarity measures between intuitionistic fuzzy sets (IFSs) and between vague sets are reviewed. A numerical example shows that these similarity measures are not always reasonable in some cases, and one reason is that inherent interactions among elements of a given universe are ignored. To overcome the drawbacks of these similarity measures, a new similarity measure of IFSs is proposed based on a Choquet integral model, where a generalized fuzzy measure is used to characterize interactions among elements of a given universe of IFSs or vague sets, and the Choquet integral model instead of a weighted average model is used to compute the new similarity measure. Further, properties of the new similarity measure are discussed, and numerical examples show that this new similarity measure is more reasonable than the existing similarity measures.
Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on; 11/2008
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Fuzzy Sets and Systems. 01/2003; 138:507-522.
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ABSTRACT: The Choquet integral can be regarded as one of aggregation operators being used in information fusion. In this study, we offer an interpretation of sequences of measurable functions and the Choquet integral in the framework of information fusion. Based on an efficiency measure space, we also define a new concept of a fundamental convergence in the (C) mean of sequences of measurable functions and discuss its theoretical underpinnings along with related interpretation issues as well as deliver some new results. Furthermore, an application of this concept is discussed in the context of information fusion. More specifically, based on the theoretical investigations, this idea is applied to the determination of a measurable function being used in the Choquet integral.
Journal of Mathematical Analysis and Applications.
