# A Study imbalance handling by various data sampling methods in binary classification

arXiv: https://arxiv.org/abs/2105.10959 · HTML: https://mlsanity.com/paper/2105.10959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 1 of 1)

- [Anomaly Detection in the Presence of Missing Values](https://mlsanity.com/paper/1809.01605/) (2018)

## Abstract (arXiv)

The purpose of this research report is to present the our learning curve and the exposure to the Machine Learning life cycle, with the use of a Kaggle binary classification data set and taking to explore various techniques from pre-processing to the final optimization and model evaluation, also we highlight on the data imbalance issue and we discuss the different methods of handling that imbalance on the data level by over-sampling and under sampling not only to reach a balanced class representation but to improve the overall performance. This work also opens some gaps for future work.