Large Margin Distribution Machine for Imbalanced Data Classification

The imbalanced data classification problem is common in many real-world supervised learning tasks, and always leads to the lower class-prediction accuracy of the minority class. In this paper, a modified large margin distribution machine is proposed by adjust the kernel matrix based on the distribution of the data near the class boundary, to improve the class-prediction accuracy. Through theoretical analysis backed by empirical study, it shows that the proposed algorithm in this paper works effectively on several UCI datasets.