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On distorted probabilities and m-separable fuzzy measures

On distorted probabilities and \(m\)-separable fuzzy measures
descriptionPublicationkeyboard_double_arrow_right Article 01 Dec 2011 English Publisher:Elsevier BVJournal:International Journal of Approximate Reasoning, volume 52, pages 1,325-1,336 (issn: 0888-613X, Copyright policy )
Authors: Yasuo Narukawa; Vicenç Torra;

doi: 10.1016/j.ijar.2011.01.004

handle: 10261/138236

On distorted probabilities and m-separable fuzzy measures

Abstract

Fuzzy measures are used in conjunction with fuzzy integrals for aggregation. Their role in the aggregation is to permit the user to express the importance of the information sources (either criteria or experts). Due to the fact that fuzzy measures are set functions, the definition of such measures requires the definition of 2n parameters, where n is the number of information sources. To make the definition easier, several families of fuzzy measures have been defined in the literature. In this paper m-separable fuzzy measures are introduced. We present some results on this type of measures and we relate them to some of the previous existing ones. We study generating functions for m-separable fuzzy measures and some properties related to these generating functions. © 2011 Elsevier Ltd. All rights reserved.

Partial support by the Spanish MEC (projects ARES CONSOLIDER INGENIO 2010 CSD2007-00004 and eAEGIS TSI2007-65406-C03-02) is acknowledged.

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Keywords

m-Dimensional distorted probabilities, Distorted probabilities, Fuzzy measure theory, Applied Mathematics, Theoretical Computer Science, Fuzzy probability, Artificial Intelligence, \(m\)-dimensional distorted probabilities, fuzzy measures, distorted probabilities, m-Symmetric fuzzy measures, \(m\)-symmetric fuzzy measures, Software, Fuzzy measures

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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