An effective Weighted Multi-class Least Squares Twin Support Vector Machine for Imbalanced data classification

DOI

10.1080/18756891.2015.1061395How to use a DOI?

Keywords

Least Squares Twin Support Vector Machine, Multi Least Squares Twin Support Vector Machine, Weighted Multi Least Squares Twin Support Vector Machine, Imbalanced data classification

Abstract

The performance of machine learning algorithms is affected by the imbalanced distribution of data among classes. This issue is crucial in various practical problem domains, for example, in medical diagnosis, network intrusion, fraud detection etc. Most efforts so far are mainly focused upon binary class imbalance problem. However, the class imbalance problem is also reported in multi-class scenario. The solutions proposed by the researchers for two-class scenario are not applicable to multi-class domains. So, in this paper, we have developed an effective Weighted Multi-class Least Squares Twin Support Vector Machine (WMLSTSVM) approach to address the problem of imbalanced data classification for multi class. This research work employs appropriate weight setting in loss function, e.g. it adjusts the cost of error for imbalanced data in order to control the sensitivity of the classifier. In order to prove the validity of the proposed approach, the experiment has been performed on fifteen benchmark datasets. The performance of proposed WMLSTSVM is analyzed and compared with some other SVMs and TWSVMs and it is observed that our proposed approach outperforms all of them. The proposed approach is statistically analyzed by using non-parametric Wilcoxon signed rank and Friedman tests.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Divya Tomar
AU  - Sonali Agarwal
PY  - 2015
DA  - 2015/08/01
TI  - An effective Weighted Multi-class Least Squares Twin Support Vector Machine for Imbalanced data classification
JO  - International Journal of Computational Intelligence Systems
SP  - 761
EP  - 778
VL  - 8
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1061395
DO  - 10.1080/18756891.2015.1061395
ID  - Tomar2015
ER  -