Combining Re Sampling with Twin Support Vector Machine for 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. 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.
2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)