An Improved SVM-KM Model for Imbalanced Datasets
Deng Weiguo, Wang Li, Wang Yiyang, Qian Zhong · 2012
Support vector machine is a widely used machine learning technique. SVM-KM model can speed SVM training by eliminating non support vectors, but imbalanced datasets will affect the classification accuracy. In this paper, we proposed an improved SVM-KM model, which assign different error costs to different classes. Based on the simulation results, the improved SVM-KM model performed best for imbalanced datasets.