Imbalanced Data Classification with Deep Support Vector Machines

In recent years, deep learning has become increasingly popular in various fields. However, the performance of deep learning on imbalanced data has not been examined. The imbalanced data is a special problem in target detection and classification task, where the number of one class is less than the other classes. This paper focuses on evaluating the performance of the deep support vector machine (DSVM) algorithm in dealing with imbalanced human target detection datasets. Furthermore, we optimize the parameters of the DSVM algorithm to obtain better detection performance. It is compared with the stacked auto-encoder (SAE) and the support vector machine (SVM) algorithm. Finally, numerical experimental results show that the DSVM algorithm can effectively capture the minority class.