Identification of fuzzy measures from sample data with genetic algorithms

Authors:
Elías F. Combarro;Pedro Miranda
Affiliations:
Artificial Intelligence Center, University of Oviedo, Spain and Despacho, Edificios Departamentales, Gijón., Spain;Department of Statistics and O. R., Complutense University of Madrid, Spain
Venue:
Computers and Operations Research
Year:
2006

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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.