Genetic feature selection for optimal functional link artificial neural network in classification

Satchidananda Dehuri, Bijan Bihari Mishra, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)

Abstract

This paper proposed a hybrid functional link artificial neural network (HFLANN) embedded with an optimization of input features for solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded selected features, HFLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer neural networks. An extensive simulation studies has been carried out to illustrate the effectiveness of this method over to its rival functional link artificial neural network (FLANN) and radial basis function (RBF) neural network.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings
Pages156-163
Number of pages8
DOIs
Publication statusPublished - 2008
Event9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008 - Daejeon, Korea, Republic of
Duration: 2008 Nov 22008 Nov 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5326 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008
CountryKorea, Republic of
CityDaejeon
Period08/11/208/11/5

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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