Learning weights for weighted OWA operators

Author

Torra, Vicenç

Author_Institution

Inst. d´´Investigacio en Intel.ligencia Artificial, CSIC, Barcelona, Spain

Volume

4

fYear

2000

fDate

2000

Firstpage

2530

Abstract

Weighted OWA (ordered weighted aggregation) operators were introduced as a generalization of the weighted mean (WM) and the OWA operators, so that the advantages of both could be used in a single data fusion function. In this work, we study the determination of their parameters when a set of examples is at our disposal. The approach presented in this paper is of interest in data mining when a certain variable has to be expressed in terms of some other ones. In this case, the learning of weights corresponds to fitting the model, and the weights correspond to the importance of the variables and of their values

Keywords

data mining; learning by example; mathematical operators; sensor fusion; data fusion function; data mining; learning from examples; model fitting; ordered weighted aggregation operator; parameter determination; value importance; variable expression; variable importance; weight learning; weighted OWA operators; weighted mean operator; Aggregates; DNA; Data mining; Fitting; Fuses; Humans; Knowledge representation; Open wireless architecture; Partitioning algorithms; Sequences;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE

Conference_Location

Nagoya

Print_ISBN

0-7803-6456-2

Type

conf

DOI

10.1109/IECON.2000.972396

Filename

972396