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


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.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN: 9789832078791 Organized by: Universiti Utara Malaysia
Uncontrolled Keywords: mixed variable ant colony optimization, support vector machine, features selection, model selection, pattern classification
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Prof. Dr. Ku Ruhana Ku Mahamud
Date Deposited: 24 Aug 2014 01:24
Last Modified: 08 Apr 2015 02:04
URI: http://repo.uum.edu.my/id/eprint/11963

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