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

Lu Cao, Hong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 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 languageEnglish
Title of host publicationProceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016
EditorsHong Shen, Hong Shen, Yingpeng Sang, Hui Tian
PublisherIEEE Computer Society
Pages325-329
Number of pages5
ISBN (Electronic)9781509050819
DOIs
Publication statusPublished - 2 Jul 2016
Externally publishedYes
Event17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016 - Guangzhou, China
Duration: 16 Dec 201618 Dec 2016

Publication series

NameParallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
Volume0

Conference

Conference17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016
Country/TerritoryChina
CityGuangzhou
Period16/12/1618/12/16

Keywords

  • Classification
  • Imbalanced dataset
  • Over-sampling
  • TWSVM
  • Under-sampling

Fingerprint

Dive into the research topics of 'Combining re-sampling with twin support vector machine for imbalanced data classification'. Together they form a unique fingerprint.

Cite this