Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.T...
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North Atlantic University Union
2013
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my.uum.repo.92192013-10-27T01:35:25Z http://repo.uum.edu.my/9219/ Incremental continuous ant colony optimization for tuning support vector machine’s parameters Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana T Technology (General) Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. Ant Colony Optimization originally deals with discrete optimization problems. Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous in nature, the values wil have to be discretized.The discretization process will result in loss of some information and, hence, affects the classification accuracy and seeks time.This paper presents an algorithm to optimize Support Vector Machine parameters using Incremental continuous Ant Colony Optimization without the need to discretize continuous values.Eight datasets from UCI were used to evaluate the performance of the proposed algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.Experimental results of the proposed algorithm also show promising performance in terms of classification accuracy and size of features subset. North Atlantic University Union 2013 Article PeerReviewed application/pdf en http://repo.uum.edu.my/9219/1/2.pdf Alwan, Hiba Basim and Ku-Mahamud, Ku Ruhana (2013) Incremental continuous ant colony optimization for tuning support vector machine’s parameters. International Journal of Computers, 7 (2). pp. 50-57. ISSN 1998-4308 http://www.naun.org/cms.action?id=6450 |
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T Technology (General) Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana Incremental continuous ant colony optimization for tuning support vector machine’s parameters |
description |
Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. Ant Colony Optimization originally deals with discrete optimization problems. Hence, in applying Ant
Colony Optimization for optimizing Support Vector Machine parameters, which are continuous in nature, the values wil have to be discretized.The discretization process will result in loss of some information and, hence, affects the classification accuracy and seeks time.This paper presents an algorithm to optimize Support Vector Machine parameters using Incremental continuous Ant Colony Optimization without the need to discretize continuous values.Eight datasets from UCI were used to evaluate the performance of the proposed algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when
compared to grid search techniques, GA with feature
chromosome-SVM, PSO-SVM, and GA-SVM.Experimental results of the proposed algorithm also show promising performance in terms of classification accuracy and size of features subset. |
format |
Article |
author |
Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana |
author_facet |
Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana |
author_sort |
Alwan, Hiba Basim |
title |
Incremental continuous ant colony optimization for tuning support vector machine’s parameters
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title_short |
Incremental continuous ant colony optimization for tuning support vector machine’s parameters
|
title_full |
Incremental continuous ant colony optimization for tuning support vector machine’s parameters
|
title_fullStr |
Incremental continuous ant colony optimization for tuning support vector machine’s parameters
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title_full_unstemmed |
Incremental continuous ant colony optimization for tuning support vector machine’s parameters
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title_sort |
incremental continuous ant colony optimization for tuning support vector machine’s parameters |
publisher |
North Atlantic University Union |
publishDate |
2013 |
url |
http://repo.uum.edu.my/9219/1/2.pdf http://repo.uum.edu.my/9219/ http://www.naun.org/cms.action?id=6450 |
_version_ |
1644280048062038016 |
score |
13.154949 |