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.

翻译：我们旨在通过实验证明，我们提出的代价敏感PEGASOS支持向量机（无需合成多数类过采样/欠采样（SMOTE））在处理多数类与少数类比例从8.6:1到130:1的不平衡数据集时，能够取得良好性能。尽管许多方法采用SMOTE技术，但我们寻求一种计算成本更低的方案。我们通过学习曲线评估性能。这些曲线有助于诊断模型是否过拟合、欠拟合，或是否选择了具有过度代表性或代表性不足的训练/测试数据。我们还将通过验证曲线研究超参数变化的影响。我们将PEGASOS代价敏感支持向量机的结果与丁氏使用其LINEAR SVM DECIDL方法分析的三个数据集进行对比。他在一个数据集上获得了0.5的ROC-AUC值，我们认为该数据集最适合使用核支持向量机。我们的工作将在丁氏研究基础上扩展，将核函数引入支持向量机。我们将使用Python而非MatLab，因为Python具备字典功能，可在多参数交叉验证过程中存储混合数据类型。