doi: 10.18178/ijfcc.2017.6.3.496
Support Vector Machine with Restarting Genetic Algorithm for Classifying Imbalanced Data
Keerachart Suksut
1,*
,
Kittisak Kerdprasop
2
,
Nittaya Kerdprasop
2
- 1 School of Computer Engineering, Suranaree University of Technology (SUT), 111 University Avenue, Muang, Nakhon Ratchasima 30000, Thailand.
- 2 School of Computer Engineering
Abstract
Algorithms for data classification are normally attheir high performance when the dataset has good balance inwhich the number of data instances in each class isapproximately equal. But when the dataset is imbalanced, theclassification model tends to bias toward the majority class. Thegoal of imbalanced data classification is how to improve theperformance of a model to better recognize data from minorityclass, especially when minority is more interesting than themajority data. In this research, we propose technique forbalancing data with hybrid resampling techniques and thenperform parameter optimization with restarting geneticalgorithm. The optimized parameters are for support vectormachine to induce efficient model for recognizing data inminority class, whereas maintaining overall accuracy. Theexperimental results show that the proposed technique has highperformance than others.
Keywords
- Imbalanced data
- restarting genetic algorithm
- support vector machine
How to Cite
Keerachart Suksut, Kittisak Kerdprasop, and Nittaya Kerdprasop, "Support Vector Machine with Restarting Genetic Algorithm for Classifying Imbalanced Data," International Journal of Future Computer and Communication, vol. 6, no. 3, pp. 92-96, 2017. https://doi.org/ 10.18178/ijfcc.2017.6.3.496
Copyright & License
Copyright © 2017 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).