# Benchmark of Data Preprocessing Methods for Imbalanced Classification

arXiv: https://arxiv.org/abs/2303.03094 · HTML: https://mlsanity.com/paper/2303.03094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 58 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Oversampling for Imbalanced Learning Based on K-Means and SMOTE](https://mlsanity.com/paper/1711.00837/) (2017)
- [A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection](https://mlsanity.com/paper/2203.09938/) (2022)
- [On Model Evaluation under Non-constant Class Imbalance](https://mlsanity.com/paper/2001.05571/) (2020)
- [Imbalance Learning for Variable Star Classification](https://mlsanity.com/paper/2002.12386/) (2020)
- [Bad practices in evaluation methodology relevant to class-imbalanced problems](https://mlsanity.com/paper/1812.01388/) (2018)
- [Decision-forest voting scheme for classification of rare classes in network intrusion detection](https://mlsanity.com/paper/2107.11862/) (2021)

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

- [Command-line Obfuscation Detection using Small Language Models](https://mlsanity.com/paper/2408.02637/) (2024)

## Abstract (arXiv)

Severe class imbalance is one of the main conditions that make machine learning in cybersecurity difficult. A variety of dataset preprocessing methods have been introduced over the years. These methods modify the training dataset by oversampling, undersampling or a combination of both to improve the predictive performance of classifiers trained on this dataset. Although these methods are used in cybersecurity occasionally, a comprehensive, unbiased benchmark comparing their performance over a variety of cybersecurity problems is missing. This paper presents a benchmark of 16 preprocessing methods on six cybersecurity datasets together with 17 public imbalanced datasets from other domains. We test the methods under multiple hyperparameter configurations and use an AutoML system to train classifiers on the preprocessed datasets, which reduces potential bias from specific hyperparameter or classifier choices. Special consideration is also given to evaluating the methods using appropriate performance measures that are good proxies for practical performance in real-world cybersecurity systems. The main findings of our study are: 1) Most of the time, a data preprocessing method that improves classification performance exists. 2) Baseline approach of doing nothing outperformed a large portion of methods in the benchmark. 3) Oversampling methods generally outperform undersampling methods. 4) The most significant performance gains are brought by the standard SMOTE algorithm and more complicated methods provide mainly incremental improvements at the cost of often worse computational performance.