Alwan, Hiba Basim and Ku-Mahamud, Ku Ruhana (2012) Incremental continuous ant colony optimization technique for support vector machine model selection problem. In: 3rd International Conference on Applied Mathematics and Informatics, 29-31 December 2012, Montreux, Switzerland.

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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 Incremental Continuous Ant Colony Optimization without the need to discretize continuous value for support vector machine parameters.Seven datasets from UCI were used to evaluate the credibility of the proposed hybrid algorithmin terms of classification accuracy.Promising results were obtained when compared to grid search technique.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN: 978-1-61804-148-7
Uncontrolled Keywords:	Pattern Classification, Support Vector Machine, Continuous Ant Colony Optimization,Incremental Continuous Ant Colony Optimization, model selection, Probability Density Function
Subjects:	T Technology > T Technology (General)
Divisions:	College of Arts and Sciences
Depositing User:	Prof. Dr. Ku Ruhana Ku Mahamud
Date Deposited:	10 Jan 2013 02:02
Last Modified:	24 Dec 2013 02:15
URI:	https://repo.uum.edu.my/id/eprint/6967

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