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.
Bibliographical noteFunding Information:
Dr. S. Dehuri would like to thank Department of Science and Technology, Govt. of India, vide letter number SR/BY/E-07/2007 dated 03-01-2008 for financial support under the BOYSCAST Program 2007–2008 and the financial support of BK21 Project, Korea’s Ministry of Education and Human Research Development. Prof. S. B. Cho would like to thank the financial support of BK21 Project initiated by Korea’s Ministry of Education and Human Resource Development.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence