A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets

Satchidananda Dehuri, Sung-Bae Cho

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

This paper proposed a hybrid genetic based functional link artificial neural network (HFLANN) with simultaneous optimization of input features for the purpose of 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 of selected features, HFLANN overcomes the nonlinearity nature of problems, which is commonly encountered in single-layer neural networks. The features like simplicity of the architecture and low computational complexity of the network encourage us to use it in classification task of data mining. Further, the issue of statistical tests for comparison of algorithms on multiple datasets, which is even more essential to typical machine learning and data mining studies, has been all but ignored. In this work, we recommend a set of simple, yet safe and robust parametric and nonparametric tests for statistical comparisons of HFLANN with FLANN and RBF classifiers over multiple datasets by an extensive simulation studies.

Original languageEnglish
Pages (from-to)317-328
Number of pages12
JournalNeural Computing and Applications
Volume19
Issue number2
DOIs
Publication statusPublished - 2010 Jan 1

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Classifiers
Neural networks
Data mining
Statistical tests
Set theory
Learning systems
Computational complexity
Genetic algorithms

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software

Cite this

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A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. / Dehuri, Satchidananda; Cho, Sung-Bae.

In: Neural Computing and Applications, Vol. 19, No. 2, 01.01.2010, p. 317-328.

Research output: Contribution to journalArticle

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