Towards crafting an improved functional link artificial neural network based on differential evolution and feature selection

Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, Sung Bae Cho, Gi Nam Wang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The proposed work describes an improved functional link artificial neural network (FLANN) for classification. The improvement in terms of classification accuracy of the network is realized through differential evolution (DE) and filter based feature selection approach. Information gain theory is used to filter out irrelevant features and provide relevant features to the functional expansion unit of FLANN as an input, which in turn maps low to high dimensional feature space for constructing an improved classifier. To fine tune the weight vector of the given network, differential evolution is used. The work is validated using skewed and balanced dataset retrieved from the University of California Irvine (UCI) repository. Our systematic experimental study divulges that the performance of the differential-evolution trained FLANN is promising than genetic algorithm trained FLANN, ISO-FLANN, and PSO-BP.

Original languageEnglish
Pages (from-to)195-208
Number of pages14
JournalInformatica (Slovenia)
Volume39
Issue number2
Publication statusPublished - 2015 Jan 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Artificial Intelligence

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