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Solving Support Vector Machine Model Selection Problem Using Continuous Ant Colony Optimization


Alwan, Hiba Basim and Ku-Mahamud, Ku Ruhana (2013) Solving Support Vector Machine Model Selection Problem Using Continuous Ant Colony Optimization. International Journal of Information Processing and Management, 4 (2). pp. 86-97. ISSN 2093-4009

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Abstract

Ant Colony Optimization has been used to solve Support Vector Machine model selection problem.Ant Colony Optimization originally deals with discrete optimization problem.In applying Ant Colony Optimization for optimizing Support Vector Machine parameters which are continuous variables, there is a need to discretize the continuously value into discrete value.This discretize process would result in loss of some information and hence affect the classification accuracy and seeking time.This study proposes an algorithm that can optimize Support Vector Machine parameters using Continuous Ant Colony Optimization without the need to discretize continuous value for Support Vector Machine parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed hybrid algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.

Item Type: Article
Uncontrolled Keywords: Support Vector Machine, Continuous Ant Colony Optimization, Parameters Optimization
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 24 Dec 2013 02:28
Last Modified: 24 Dec 2013 02:28
URI: http://repo.uum.edu.my/id/eprint/9843

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