A Hybrid Approach for Feature Subset Selection using Neural Networks and Ant Colony Optimization
Abstract
One of the significant research problems in multivariate analysis is the selection of a subset of input variables that can predict the desired output with an acceptable level of accuracy. This goal is attained through the elimination of the variables that produce noise or, are strictly correlated with other already selected variables. Feature subset selection (selection of the input variables) is important in correlation analysis and in the field of classification and modeling. This paper presents a hybrid method based on ant colony optimization and artificial neural networks (ANNs) to address feature selection. the proposed hybrid model is demonstrated using data sets from the domain of medical diagnosis, yielding promising results. © 2006 Elsevier Ltd. All rights reserved.
Recommended Citation
R. K. Sivagaminathan and S. Ramakrishnan, "A Hybrid Approach for Feature Subset Selection using Neural Networks and Ant Colony Optimization," Expert Systems with Applications, vol. 33, no. 1, pp. 49 - 60, Elsevier, Jul 2007.
The definitive version is available at https://doi.org/10.1016/j.eswa.2006.04.010
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Ant colony optimization; Feature subset selection; Neural networks
International Standard Serial Number (ISSN)
0957-4174
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jul 2007
