A hybrid approach for feature subset selection using neural networks and ant colony optimization

Authors:
Rahul Karthik Sivagaminathan;Sreeram Ramakrishnan
Affiliations:
Department of Engineering Management and Systems Engineering, 1870 Miner Circle, University of Missouri - Rolla, Rolla, MO 65409, USA;Department of Engineering Management and Systems Engineering, 1870 Miner Circle, University of Missouri - Rolla, Rolla, MO 65409, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

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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.