Primal Estimated Subgradient Solver for SVM for Imbalanced Classification

We aim to demonstrate in experiments that our cost sensitive PEGASOS SVM (without synthetic majority oversampling/under sampling (SMOTE) ) achieves good performance on imbalanced data sets with a Majority to Minority Ratio ranging from 8.6:1 to 130:1. Although many resort to SMOTE methods, we aim for a less computationally intensive method. We evaluate the performance by examining the learning curves. These curves diagnose whether we overfit or underfit or we choose over-representive or under representative training/test data. We will also examine the effect of varying the hyperparameters via validation curves. We compare our PEGASOS Cost-Sensitive SVM's results on three of the datasets Ding analyzed using his LINEAR SVM DECIDL method. He obtained an ROC-AUC of .5 in one dataset. We consider that dataset the most promising use of kernel Support Vector Machine. Our work will extend the work of Ding by incorporating kernels into Support Vector Machine. We will use Python rather than MatLab as python has dictionaries for storing mixed data types during multi-parameter cross-validation.