Multiclass SVM with ramp loss for imbalanced data classification
Piyaphol Phoungphol, Yanqing Zhang, Yichuan Zhao, Bismita Srichandan · 2012
Class imbalance is a common problem encountered in applying machine learning tools to real-world data. It causes most classifiers to perform sub-optimally and yield very poor performance when a dataset is highly imbalance. In this paper, we study a new method of formulating a multi class SVM problem for imbalanced dataset to improve the classification performance. The proposed method applies costsensitive approach and ramp loss function to the Crammer & Singer multiclass SVM formulation. Experimental results on multiple VCI datasets show that the proposed solution can effectively cure the problem when the datasets are noisy and highly imbalanced.