期刊
COMPUTER SCIENCE AND INFORMATION SYSTEMS
卷 14, 期 3, 页码 579-595出版社
COMSIS CONSORTIUM
DOI: 10.2298/CSIS161221017L
关键词
over-sampling; under-sampling; imbalanced dataset; TWSVM; classification
资金
- 985 Project - Sun Yat-sen University
- Australian Research Council [DP150104871]
- Youth Innovation Talent Project of Guangdong Province [2015KQNCX172]
- Science and Technology Project of Jiangmen City [2015[138], 2016[189]]
- Youth Foundation of Wuyi University [2015zk11]
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 over-sampling 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.
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