# Handling Imbalanced Data: A Case Study for Binary Class Problems

arXiv: https://arxiv.org/abs/2010.04326 · HTML: https://mlsanity.com/paper/2010.04326/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [A Comparative Analysis of Multiple Methods for Predicting a Specific Type of Crime in the City of Chicago](https://mlsanity.com/paper/2304.13464/) (2023)
- [Categorising Products in an Online Marketplace: An Ensemble Approach](https://mlsanity.com/paper/2304.13852/) (2023)

## Abstract (arXiv)

For several years till date, the major issues in terms of solving for classification problems are the issues of Imbalanced data. Because majority of the machine learning algorithms by default assumes all data are balanced, the algorithms do not take into consideration the distribution of the data sample class. The results tend to be unsatisfactory and skewed towards the majority sample class distribution. This implies that the consequences as a result of using a model built using an Imbalanced data without handling for the Imbalance in the data could be misleading both in practice and theory. Most researchers have focused on the application of Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) Sampling Approach in handling data Imbalance independently in their works and have failed to better explain the algorithms behind these techniques with computed examples. This paper focuses on both synthetic oversampling techniques and manually computes synthetic data points to enhance easy comprehension of the algorithms. We analyze the application of these synthetic oversampling techniques on binary classification problems with different Imbalanced ratios and sample sizes.