Multiple-criteria genetic algorithms for feature selection in neurofuzzy modeling

Christos Emmanouilidis*, Andrew Hunter, John MacIntyre, Chris Cox

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

18 Citaten (Scopus)

Samenvatting

This paper discusses the use of multiple-criteria genetic algorithms for feature selection in classification problems. This feature selection approach is shown to yield a diverse population of alternative feature subsets with various accuracy/complexity trade-off. The algorithm is applied to select features for performing classification with fuzzy models, and is evaluated on two real-world data sets. We discuss when multiple-criteria genetic algorithm feature selection is preferable to a sequential feature selection procedure, namely backwards elimination. Among the key features of the presented approach are its computational simplicity, effectiveness on real world problems and the potential it has to become a powerful tool aiding many empirical modeling and data mining processes.

Originele taal-2English
TitelIJCNN'99
Subtitelproceedings, International Joint Conference on Neural Networks, Washington, DC, July 10-16, 1999
UitgeverijIEEE
Pagina's4387-4392
Aantal pagina's6
ISBN van geprinte versie0-7803-5529-6
DOI's
StatusPublished - 1999
Extern gepubliceerdJa
EvenementInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duur: 10-jul-199916-jul-1999

Publicatie series

NaamProceedings of the International Joint Conference on Neural Networks
Volume6

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'99)
StadWashington, DC, USA
Periode10/07/199916/07/1999

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