We aim to demonstrate in experiments that our cost sensitive PEGASOS SVM achieves good performance on imbalanced data sets with a Majority to Minority Ratio ranging from 8.6:1 to 130:1 and to ascertain whether the including intercept (bias), regularization and parameters affects performance on our selection of datasets. 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 representative or under representative training/test data. We will also see the background of the hyperparameters versus the test and train error in validation curves. We benchmark our PEGASOS Cost-Sensitive SVM's results of Ding's LINEAR SVM DECIDL method. He obtained an ROC-AUC of .5 in one dataset. Our work will extend the work of Ding by incorporating kernels into SVM. 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，我们的代价敏感PEGASOS SVM在多数类与少数类比例从8.6:1到130:1的不平衡数据集上能够取得良好的性能，并验证包含截距（偏置）、正则化及参数是否会影响所选数据集的性能。尽管许多研究采用SMOTE方法，但我们的目标是寻找计算复杂度更低的方法。我们通过检查学习曲线来评估性能。这些曲线能够诊断模型是否存在过拟合或欠拟合，以及训练/测试数据是否具有代表性或偏误。我们还将通过验证曲线了解超参数背景与测试误差及训练误差之间的关系。我们以Ding的LINEAR SVM DECIDL方法为基准，比较我们的PEGASOS代价敏感SVM的结果。他在一个数据集上取得了0.5的ROC-AUC值。我们的工作将通过在SVM中引入核函数来扩展Ding的研究。我们将使用Python而非MATLAB，因为Python具有字典功能，便于在多参数交叉验证过程中存储混合数据类型。