@inproceedings{39a1bb1a57a84a228224871f85b84113,
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",
month = jul,
day = "2",
doi = "10.1109/PDCAT.2016.076",
language = "English",
series = "Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings",
publisher = "IEEE Computer Society",
pages = "325--329",
editor = "Hong Shen and Hong Shen and Yingpeng Sang and Hui Tian",
booktitle = "Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016",
address = "United States",
}