Formulating new enhanced pattern classification algorithms based on ACO-SVM
This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The fi...
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my.uum.repo.98452013-12-24T02:48:19Z http://repo.uum.edu.my/9845/ Formulating new enhanced pattern classification algorithms based on ACO-SVM Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana QA76 Computer software This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset. North Atlantic University Union NAUN 2013 Article PeerReviewed application/pdf en http://repo.uum.edu.my/9845/1/p.pdf Alwan, Hiba Basim and Ku-Mahamud, Ku Ruhana (2013) Formulating new enhanced pattern classification algorithms based on ACO-SVM. International Journal of Mathematical Models and Methods in Applied Sciences, 7 (7). pp. 700-707. ISSN 19980140 http://www.scimagojr.com/journalsearch.php?q=18100156703&tip=sid&clean=0 |
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QA76 Computer software Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana Formulating new enhanced pattern classification algorithms based on ACO-SVM |
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This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental
Continuous Ant Colony Optimization (IACOR) and Incremental
Mixed Variable Ant Colony Optimization (IACOMV) with Support
Vector Machine (SVM) to enhance the performance of SVM.The
first algorithm aims to solve SVM model selection problem. ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize
continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification
accuracy with small size of features subset. |
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Article |
author |
Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana |
author_facet |
Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana |
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Alwan, Hiba Basim |
title |
Formulating new enhanced pattern classification algorithms based on ACO-SVM |
title_short |
Formulating new enhanced pattern classification algorithms based on ACO-SVM |
title_full |
Formulating new enhanced pattern classification algorithms based on ACO-SVM |
title_fullStr |
Formulating new enhanced pattern classification algorithms based on ACO-SVM |
title_full_unstemmed |
Formulating new enhanced pattern classification algorithms based on ACO-SVM |
title_sort |
formulating new enhanced pattern classification algorithms based on aco-svm |
publisher |
North Atlantic University Union NAUN |
publishDate |
2013 |
url |
http://repo.uum.edu.my/9845/1/p.pdf http://repo.uum.edu.my/9845/ http://www.scimagojr.com/journalsearch.php?q=18100156703&tip=sid&clean=0 |
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13.149126 |