Primal Estimated Subgradient Solver for SVM for Imbalanced Classification. (arXiv:2206.09311v2 [cs.LG] UPDATED)

We aim to demonstrate in experiments that our cost-sensitive PEGASOS SVM
(without synthetic majority oversampling/undersampling (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
computational intensive method. We evaluate the performance by examining the
learning curves. These curves diagnose whether we overfit or underfit or we
choose overrepresentatuve or underrepresentative training/test data. We will
also examine the effect …

arxiv classification solver svm