Imbalanced Data Classification Based on Hybrid Resampling and Twin Support Vector Machine
Computer Science and Information Systems, Tome 14 (2017) no. 3
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Imbalanced datasets exist widely in real life. The identification of the minority class in imbalanced datasets tends to be the focus of classification. As a variant of enhanced support vector machine (SVM), the twin support vector machine (TWSVM) provides an effective technique for data classification. TWSVM is based on a relative balance in the training sample dataset and distribution to improve the classification accuracy of the whole dataset, however, it is not effective in dealing with imbalanced data classification problems. In this paper, we propose to combine a re-sampling technique, which utilizes oversampling and under-sampling to balance the training data, with TWSVM to deal with imbalanced data classification. Experimental results show that our proposed approach outperforms other state-of-art methods.
Keywords:
over-sampling, under-sampling, imbalanced dataset, TWSVM, classification
Lu Cao; Hong Shen. Imbalanced Data Classification Based on Hybrid Resampling and Twin Support Vector Machine. Computer Science and Information Systems, Tome 14 (2017) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2017_14_3_a3/
@article{CSIS_2017_14_3_a3,
author = {Lu Cao and Hong Shen},
title = {Imbalanced {Data} {Classification} {Based} on {Hybrid} {Resampling} and {Twin} {Support} {Vector} {Machine}},
journal = {Computer Science and Information Systems},
year = {2017},
volume = {14},
number = {3},
url = {http://geodesic.mathdoc.fr/item/CSIS_2017_14_3_a3/}
}