  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
whether the random sample of data chosen during the process was not random
enough or diverse enough in dependent variable class for the algorithm to
generalized to unseen examples. 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.
