A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

2011 ◽  
Vol 38 (7) ◽  
pp. 8580-8585 ◽  
Author(s):  
Jae Pil Hwang ◽  
Seongkeun Park ◽  
Euntai Kim
Keyword(s):  
Support Vector Machine ◽  
Cost Function ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Problem ◽  
Support Vector ◽  
Quadratic Cost ◽  
Imbalanced Data Classification ◽  
Quadratic Cost Function

Imbalanced data classification algorithm with support vector machine kernel extensions

2018 ◽  
Vol 12 (3) ◽  
pp. 341-347 ◽  
Author(s):  
Feng Wang ◽  
Shaojiang Liu ◽  
Weichuan Ni ◽  
Zhiming Xu ◽  
Zemin Qiu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Algorithm ◽  
Support Vector ◽  
Imbalanced Data Classification

An efficient weighted Lagrangian twin support vector machine for imbalanced data classification

2014 ◽  
Vol 47 (9) ◽  
pp. 3158-3167 ◽  
Author(s):  
Yuan-Hai Shao ◽  
Wei-Jie Chen ◽  
Jing-Jing Zhang ◽  
Zhen Wang ◽  
Nai-Yang Deng
Keyword(s):  
Support Vector Machine ◽  
Imbalanced Data ◽  
Data Classification ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Imbalanced Data Classification

scholarly journals Imbalanced data classification based on hybrid resampling and twin support vector machine

2017 ◽  
Vol 14 (3) ◽  
pp. 579-595 ◽  
Author(s):  
Lu Cao ◽  
Hong Shen
Keyword(s):  
Support Vector Machine ◽  
Real Life ◽  
Imbalanced Data ◽  
Data Classification ◽  
Training Data ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Imbalanced Datasets ◽  
Minority Class ◽  
Imbalanced Data Classification

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.

Sign in / Sign up

Export Citation Format

Share Document