<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper presents a new approach to feature selection and classifcation based on support vector machine and hybrid artificial bee colony. The approach consists of two stages. At the first stage, this paper presented a hybrid artificial bee colony-based classifier model that combines artificial bee colony to improve classification accuracy with the most superior model parameter and features were selected from the original feature set. The classification accuracy and the feature subset provided by the SVM classifier are both considered to update the food source. Finally, the most superior features and optimal model parameter are fed into SVM to identify different class. The testing results verify the effectiveness of the method in extracting feature subset and pattern classification</p>
</abstract>

This paper presents a new approach to feature selection and classifcation based on support vector machine and hybrid artificial bee colony. The approach consists of two stages. At the first stage, this paper presented a hybrid artificial bee colony-based classifier model that combines artificial bee colony to improve classification accuracy with the most superior model parameter and features were selected from the original feature set. The classification accuracy and the feature subset provided by the SVM classifier are both considered to update the food source. Finally, the most superior features and optimal model parameter are fed into SVM to identify different class. The testing results verify the effectiveness of the method in extracting feature subset and pattern classification

An Intelligent Feature Selection and Classification Method Based on Hybrid ABC–SVM

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper presents a new approach to feature selection and classifcation based on support vector machine and hybrid artificial bee colony. The approach...

An Intelligent Feature Selection and Classification Method Based on Hybrid ABC–SVM

Published Online: Dec 01, 2016

Page range: 1859 - 1876

Received: Jul 17, 2016

Accepted: Oct 16, 2016

DOI: https://doi.org/10.21307/ijssis-2017-943

Keywords
Hybrid Artificial Bee Colony, Pattern classification, Feature Selection, Parameter Optimization

© 2016 Jie Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Intelligent Feature Selection and Classification Method Based on Hybrid ABC–SVM

Published Online: Dec 01, 2016

Page range: 1859 - 1876

Received: Jul 17, 2016

Accepted: Oct 16, 2016

DOI: https://doi.org/10.21307/ijssis-2017-943

KeywordsHybrid Artificial Bee Colony, Pattern classification, Feature Selection, Parameter Optimization

© 2016 Jie Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Hybrid Artificial Bee Colony, Pattern classification, Feature Selection, Parameter Optimization