# Efficient Hybrid Oversampling and Intelligent Undersampling for Imbalanced Big Data Classification

arXiv: https://arxiv.org/abs/2310.05789 · HTML: https://mlsanity.com/paper/2310.05789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 51 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning](https://mlsanity.com/paper/2008.09202/) (2020)
- [MLlib: Machine Learning in Apache Spark](https://mlsanity.com/paper/1505.06807/) (2015)
- [Enabling Smart Data: Noise filtering in Big Data classification](https://mlsanity.com/paper/1704.01770/) (2017)

## Built on by (most cited first, 1 of 1)

- [Neighbor displacement-based enhanced synthetic oversampling for multiclass imbalanced data](https://mlsanity.com/paper/2501.04099/) (2025)

## Abstract (arXiv)

Imbalanced classification is a well-known challenge faced by many real-world applications. This issue occurs when the distribution of the target variable is skewed, leading to a prediction bias toward the majority class. With the arrival of the Big Data era, there is a pressing need for efficient solutions to solve this problem. In this work, we present a novel resampling method called SMOTENN that combines intelligent undersampling and oversampling using a MapReduce framework. Both procedures are performed on the same pass over the data, conferring efficiency to the technique. The SMOTENN method is complemented with an efficient implementation of the neighborhoods related to the minority samples. Our experimental results show the virtues of this approach, outperforming alternative resampling techniques for small- and medium-sized datasets while achieving positive results on large datasets with reduced running times.