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


Alwan, Hiba Basim and Ku-Mahamud, Ku Ruhana (2017) Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter. International Journal of Bio-Inspired Computation, 9 (1). p. 53. ISSN 1758-0366

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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. ACOMV-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.

Item Type: Article
Uncontrolled Keywords: ACOMV; Bio-inspired computation; Feature subset selection; Mixed-variable ACO; Support vector machine; SVM; Tuning SVM parameters
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: School of Computing
Depositing User: Prof. Dr. Ku Ruhana Ku Mahamud
Date Deposited: 07 May 2017 06:58
Last Modified: 07 May 2017 06:58
URI: http://repo.uum.edu.my/id/eprint/21974

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