Imbalanced data set is a problem often found and well-studied in financial industry. In this paper, we reviewed and compared some popular methodologies handling data imbalance. We then applied the under-sampling/over-sampling methodologies to several modeling algorithms on UCI and Keel data sets. The performance was analyzed for class-imbalance methods, modeling algorithms and grid search criteria comparison.
@article{arxiv.2104.02240,
title = {Survey of Imbalanced Data Methodologies},
author = {Lian Yu and Nengfeng Zhou},
journal= {arXiv preprint arXiv:2104.02240},
year = {2021}
}