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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer-Verlag

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DOI

10.1002/chin.200301040

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1433-3058

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A hybrid genetic based functional link artificial

Authors: Satchidananda Dehuri SungBae Cho
Publish Date: 2009/10/24
Volume: 19, Issue: 2, Pages: 317-328
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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 singlelayer 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

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