RT Journal Article
T1 Identification of fuzzy measures from sample data with genetic algorithms
A1 Combarro, Elías F.
A1 Miranda Menéndez, Pedro
AB 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.
PB Pergamon-Elsevier Science Ltd
SN 0305-0548
YR 2006
FD 2006
LK https://hdl.handle.net/20.500.14352/50188
UL https://hdl.handle.net/20.500.14352/50188
LA eng
NO FEDER-MCYT
DS Docta Complutense
RD 8 abr 2025