Genetic Feature Selection for Optimal Functional Link Artificial Neural Network in Classification

  • Satchidananda Dehuri
  • Bijan Bihari Mishra
  • Sung-Bae Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)


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.


Classification Data mining Genetic algorithm FLANN RBF 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Satchidananda Dehuri
    • 1
  • Bijan Bihari Mishra
    • 2
  • Sung-Bae Cho
    • 1
  1. 1.Soft Computing Laboratory, Department of Computer ScienceYonsei UniversitySeoulKorea
  2. 2.Department of Computer Science and EngineeringCollege of Engineering BhubaneswarPatiaIndia

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