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https://hdl.handle.net/2440/134361
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Type: | Journal article |
Title: | Imbalanced data classification based on hybrid re-sampling and twin support vector machine |
Author: | Cao, L. Shen, H. |
Citation: | Computer Science and Information Systems, 2017; 14(3):579-595 |
Publisher: | ComSIS Consortium |
Issue Date: | 2017 |
ISSN: | 1820-0214 2406-1018 |
Statement of Responsibility: | Lu Cao, and Hong Shen |
Abstract: | 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. |
Rights: | Copyright status unknown |
DOI: | 10.2298/CSIS161221017L |
Grant ID: | http://purl.org/au-research/grants/arc/DP150104871 |
Published version: | http://dx.doi.org/10.2298/csis161221017l |
Appears in Collections: | Computer Science publications |
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