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• W2035145079 abstract "In this paper, we introduce a method for the identification of fuzzy measures from sample data. It is implemented using genetic algorithms and is flexible enough to allow the use of different subfamilies of fuzzy measures for the learning, as k-additive or p-symmetric measures. The experiments performed to test the algorithm suggest that it is robust in situations where there exists noise in the considered data. We also explore some possibilities for the choice of the initial population, which lead to the study of the extremes of some subfamilies of fuzzy measures, as well as the proposal of a method for random generation of fuzzy measures." @default.
• W2035145079 created "2016-06-24" @default.
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• W2035145079 date "2006-10-01" @default.
• W2035145079 modified "2023-10-18" @default.
• W2035145079 title "Identification of fuzzy measures from sample data with genetic algorithms" @default.
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• W2035145079 doi "https://doi.org/10.1016/j.cor.2005.02.034" @default.
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