Multiple-criteria genetic algorithms for feature selection in neurofuzzy modeling

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

18 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIJCNN'99
Subtitle of host publicationproceedings, International Joint Conference on Neural Networks, Washington, DC, July 10-16, 1999
PublisherIEEE
Pages4387-4392
Number of pages6
ISBN (Print)0-7803-5529-6
DOIs
Publication statusPublished - 1999
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 10-Jul-199916-Jul-1999

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume6

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period10/07/199916/07/1999

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