Article: Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter Journal: International Journal of Bio-Inspired Computation (IJBIC) 2017 Vol.9 No.1 pp.53 - 63 Abstract: This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine. A new direction for ant colony optimisation is to optimise mixed (discrete and continuous) variables. The optimised variables are then feed into selecting feature subset and tuning its parameters are two main problems of SVM. Most approaches related to tuning support vector machine parameters will discretise the continuous value of the parameters which will give a negative effect on the performance. The objective of this paper is to formulate an algorithm for tuning SVM parameters and feature subset selection. This can be achieved by simultaneously performing the selection of feature subset and tuning SVM parameters tasks. ACO<SUB align="right"><SMALL>MV</SMALL></SUB>-SVM algorithm is able to simultaneously tune SVM parameters and feature subset selection. Experimental results obtained from the proposed algorithm are better compared with other approaches in terms of classification accuracy and feature subset selection. The work in this paper also contributes to a new direction for ACO that can deal with mixed variable ACO. Inderscience Publishers - linking academia, business and industry through research

Title: Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter

Authors: Hiba Basim Alwan; Ku Ruhana Ku-Mahamud

Addresses: Department of Software Engineering, Al-Mansour University College, 69005 Nidhal Street, Baghdad, Iraq ' School of Computing, College of Art and Sciences, University Utara Malaysia, 06010 Sintok, Kedah, Malaysia

Abstract: This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine. A new direction for ant colony optimisation is to optimise mixed (discrete and continuous) variables. The optimised variables are then feed into selecting feature subset and tuning its parameters are two main problems of SVM. Most approaches related to tuning support vector machine parameters will discretise the continuous value of the parameters which will give a negative effect on the performance. The objective of this paper is to formulate an algorithm for tuning SVM parameters and feature subset selection. This can be achieved by simultaneously performing the selection of feature subset and tuning SVM parameters tasks. ACO_MV-SVM algorithm is able to simultaneously tune SVM parameters and feature subset selection. Experimental results obtained from the proposed algorithm are better compared with other approaches in terms of classification accuracy and feature subset selection. The work in this paper also contributes to a new direction for ACO that can deal with mixed variable ACO.

Keywords: mixed-variable ACO; ACOMV; ant colony optimisation; support vector machines; SVM; parameter tuning; feature subsets; feature selection; subset selection; bio-inspired computation; metaheuristics; swarm intelligence.

DOI: 10.1504/IJBIC.2017.081842

International Journal of Bio-Inspired Computation, 2017 Vol.9 No.1, pp.53 - 63

Received: 26 Jun 2014
Accepted: 09 Apr 2015
Published online: 29 Jan 2017 *

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Title: Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter

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