Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier
Support Vector Machine (SVM) is a present day classification approach originated from statistical approaches.Two main problems that influence the performance of SVM are selecting feature subset and SVM model selection. In order to enhance SVM performance, these problems must be solved simultaneously...
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| Main Authors: | , |
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| Format: | Article |
| Language: | en |
| Published: |
North Atlantic University Union NAUN
2013
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/9846/1/pa.pdf https://repo.uum.edu.my/id/eprint/9846/ http://www.scimagojr.com/journalsearch.php?q=17700156720&tip=sid&clean=0 |
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| Summary: | Support Vector Machine (SVM) is a present day classification approach originated from statistical approaches.Two main problems that influence the performance of SVM are selecting feature subset and SVM model selection. In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous
values and simultaneously solve the two problems.This paper
presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features. |
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