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

Content Provider	Springer Nature Link
Author	Dehuri, Satchidananda Cho, Sung Bae
Copyright Year	2009
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.
Starting Page	317
Ending Page	328
Page Count	12
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	19
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-10-24
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification Data mining Genetic algorithm FLANN RBF Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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