Abstract—Algorithms for data classification are normally at
their high performance when the dataset has good balance in
which the number of data instances in each class is
approximately equal. But when the dataset is imbalanced, the
classification model tends to bias toward the majority class. The
goal of imbalanced data classification is how to improve the
performance of a model to better recognize data from minority
class, especially when minority is more interesting than the
majority data. In this research, we propose technique for
balancing data with hybrid resampling techniques and then
perform parameter optimization with restarting genetic
algorithm. The optimized parameters are for support vector
machine to induce efficient model for recognizing data in
minority class, whereas maintaining overall accuracy. The
experimental results show that the proposed technique has high
performance than others.
Index Terms—Imbalanced data, restarting genetic algorithm,
support vector machine.
K. Suksut is with the School of Computer Engineering, Suranaree
University of Technology (SUT), 111 University Avenue, Muang, Nakhon
Ratchasima 30000, Thailand (corresponding author: K. Suksut; Tel.:
+66879619062; e-mail: mikaiterng@gmail.com).
K. Kerdprasop is with the School of Computer Engineering. He is also
with Knowledge Engineering Research Unit, SUT, Thailand (e-mail:
kerdpras@sut.ac.th).
N. Kerdprasop is the School of Computer Engineering. She is also with
Data Engineering Research Unit, SUT, Thailand (e-mail: nittaya@sut.ac.th).
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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.
