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 language | English |
|---|---|
| Pages (from-to) | 317-328 |
| Number of pages | 12 |
| Journal | Neural Computing and Applications |
| Volume | 19 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2010 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
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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 journal › Article
TY - JOUR
T1 - A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets
AU - Dehuri, Satchidananda
AU - Cho, Sung-Bae
PY - 2010/1/1
Y1 - 2010/1/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84859530023&partnerID=8YFLogxK
U2 - 10.1007/s00521-009-0310-y
DO - 10.1007/s00521-009-0310-y
M3 - Article
AN - SCOPUS:84859530023
VL - 19
SP - 317
EP - 328
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 2
ER -