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Mixed variable ant colony optimization technique for feature subset selection and model selection

This paper presents the integration of Mixed Variable Ant Colony Optimization and Support Vector Machine (SVM) to enhance the performance of SVM through simultaneously tuning its parameters and selecting a small number of features.The process of selecting a suitable feature subset and optimizing SV...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana
التنسيق: Conference or Workshop Item
اللغة:English
منشور في: 2013
الموضوعات:
QA76 Computer software
الوصول للمادة أونلاين:http://repo.uum.edu.my/11963/1/PID25.pdf
http://repo.uum.edu.my/11963/
http://www.icoci.cms.net.my
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id my.uum.repo.11963
record_format eprints
spelling my.uum.repo.119632015-04-08T02:04:54Z http://repo.uum.edu.my/11963/ Mixed variable ant colony optimization technique for feature subset selection and model selection Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana QA76 Computer software This paper presents the integration of Mixed Variable Ant Colony Optimization and Support Vector Machine (SVM) to enhance the performance of SVM through simultaneously tuning its parameters and selecting a small number of features.The process of selecting a suitable feature subset and optimizing SVM parameters must occur simultaneously,because these processes affect each ot her which in turn will affect the SVM performance.Thus producing unacceptable classification accuracy.Five datasets from UCI were used to evaluate the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with the small size of features subset. 2013 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/11963/1/PID25.pdf Alwan, Hiba Basim and Ku-Mahamud, Ku Ruhana (2013) Mixed variable ant colony optimization technique for feature subset selection and model selection. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28 -30 August 2013, Kuching, Sarawak, Malaysia. http://www.icoci.cms.net.my
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Alwan, Hiba Basim
Ku-Mahamud, Ku Ruhana
Mixed variable ant colony optimization technique for feature subset selection and model selection
description This paper presents the integration of Mixed Variable Ant Colony Optimization and Support Vector Machine (SVM) to enhance the performance of SVM through simultaneously tuning its parameters and selecting a small number of features.The process of selecting a suitable feature subset and optimizing SVM parameters must occur simultaneously,because these processes affect each ot her which in turn will affect the SVM performance.Thus producing unacceptable classification accuracy.Five datasets from UCI were used to evaluate the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with the small size of features subset.
format Conference or Workshop Item
author Alwan, Hiba Basim
Ku-Mahamud, Ku Ruhana
author_facet Alwan, Hiba Basim
Ku-Mahamud, Ku Ruhana
author_sort Alwan, Hiba Basim
title Mixed variable ant colony optimization technique for feature subset selection and model selection
title_short Mixed variable ant colony optimization technique for feature subset selection and model selection
title_full Mixed variable ant colony optimization technique for feature subset selection and model selection
title_fullStr Mixed variable ant colony optimization technique for feature subset selection and model selection
title_full_unstemmed Mixed variable ant colony optimization technique for feature subset selection and model selection
title_sort mixed variable ant colony optimization technique for feature subset selection and model selection
publishDate 2013
url http://repo.uum.edu.my/11963/1/PID25.pdf
http://repo.uum.edu.my/11963/
http://www.icoci.cms.net.my
_version_ 1644280784397271040
score 13.250246

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