Combining re-sampling with twin support vector machine for imbalanced data classification

Lu Cao; Hong Shen

doi:10.1109/PDCAT.2016.076

Combining re-sampling with twin support vector machine for imbalanced data classification

Lu Cao, Hong Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

Imbalanced datasets exist widely in real life. The identification of the minority class in imbalanced datasets tends to be the focus of classification. The twin support vector machine (TWSVM) as a variant of enhanced SVM provides an effective technique for data classification. In the 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.

Original language	English
Title of host publication	Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016
Editors	Hong Shen, Hong Shen, Yingpeng Sang, Hui Tian
Publisher	IEEE Computer Society
Pages	325-329
Number of pages	5
ISBN (Electronic)	9781509050819
DOIs	https://doi.org/10.1109/PDCAT.2016.076
Publication status	Published - 2 Jul 2016
Externally published	Yes
Event	17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016 - Guangzhou, China Duration: 16 Dec 2016 → 18 Dec 2016

Publication series

Name	Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
Volume	0

Conference

Conference	17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016
Country/Territory	China
City	Guangzhou
Period	16/12/16 → 18/12/16

Keywords

Classification
Imbalanced dataset
Over-sampling
TWSVM
Under-sampling

Access to Document

10.1109/PDCAT.2016.076

Cite this

Cao, L., & Shen, H. (2016). Combining re-sampling with twin support vector machine for imbalanced data classification. In H. Shen, H. Shen, Y. Sang, & H. Tian (Eds.), Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016 (pp. 325-329). Article 7943381 (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/PDCAT.2016.076

Cao, Lu ; Shen, Hong. / Combining re-sampling with twin support vector machine for imbalanced data classification. Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016. editor / Hong Shen ; Hong Shen ; Yingpeng Sang ; Hui Tian. IEEE Computer Society, 2016. pp. 325-329 (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings).

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title = "Combining re-sampling with twin support vector machine for imbalanced data classification",

abstract = "Imbalanced datasets exist widely in real life. The identification of the minority class in imbalanced datasets tends to be the focus of classification. The twin support vector machine (TWSVM) as a variant of enhanced SVM provides an effective technique for data classification. In the 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.",

keywords = "Classification, Imbalanced dataset, Over-sampling, TWSVM, Under-sampling",

author = "Lu Cao and Hong Shen",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016 ; Conference date: 16-12-2016 Through 18-12-2016",

year = "2016",

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doi = "10.1109/PDCAT.2016.076",

language = "English",

series = "Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings",

publisher = "IEEE Computer Society",

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booktitle = "Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016",

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}

Cao, L & Shen, H 2016, Combining re-sampling with twin support vector machine for imbalanced data classification. in H Shen, H Shen, Y Sang & H Tian (eds), Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016., 7943381, Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, vol. 0, IEEE Computer Society, pp. 325-329, 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016, Guangzhou, China, 16/12/16. https://doi.org/10.1109/PDCAT.2016.076

Combining re-sampling with twin support vector machine for imbalanced data classification. / Cao, Lu; Shen, Hong.
Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016. ed. / Hong Shen; Hong Shen; Yingpeng Sang; Hui Tian. IEEE Computer Society, 2016. p. 325-329 7943381 (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Imbalanced datasets exist widely in real life. The identification of the minority class in imbalanced datasets tends to be the focus of classification. The twin support vector machine (TWSVM) as a variant of enhanced SVM provides an effective technique for data classification. In the 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.

KW - Classification

KW - Imbalanced dataset

KW - Over-sampling

KW - TWSVM

KW - Under-sampling

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M3 - Conference contribution

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Cao L, Shen H. Combining re-sampling with twin support vector machine for imbalanced data classification. In Shen H, Shen H, Sang Y, Tian H, editors, Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016. IEEE Computer Society. 2016. p. 325-329. 7943381. (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings). doi: 10.1109/PDCAT.2016.076

Combining re-sampling with twin support vector machine for imbalanced data classification

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Keywords

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