# Kernel density estimation based sampling for imbalanced class distribution

arXiv: https://arxiv.org/abs/1910.07842 · HTML: https://mlsanity.com/paper/1910.07842/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models](https://mlsanity.com/paper/2408.02085/) (2024)
- [Concentration and excess risk bounds for imbalanced classification with synthetic oversampling](https://mlsanity.com/paper/2510.20472/) (2025)
- [Partial Resampling of Imbalanced Data](https://mlsanity.com/paper/2207.04631/) (2022)
- [Fast Kernel Density Estimation with Density Matrices and Random Fourier Features](https://mlsanity.com/paper/2208.01206/) (2022)
- [Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis](https://mlsanity.com/paper/2304.13275/) (2023)
- [HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling](https://mlsanity.com/paper/2509.03260/) (2025)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering](https://mlsanity.com/paper/2405.19757/) (2024)
- [Forecasting with Deep Learning: S&P 500 index](https://mlsanity.com/paper/2103.14080/) (2021)
- [Kernel density estimation-based sampling for neural network classification](https://mlsanity.com/paper/2110.12644/) (2021)
- [Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks](https://mlsanity.com/paper/2307.00507/) (2023)
- [Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications](https://mlsanity.com/paper/2405.04061/) (2024)
- [Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation](https://mlsanity.com/paper/2508.09188/) (2025)

## Abstract (arXiv)

Imbalanced response variable distribution is a common occurrence in data science. In fields such as fraud detection, medical diagnostics, system intrusion detection and many others where abnormal behavior is rarely observed the data under study often features disproportionate target class distribution. One common way to combat class imbalance is through resampling the minority class to achieve a more balanced distribution. In this paper, we investigate the performance of the sampling method based on kernel density estimation (KDE). We believe that KDE offers a more natural way of generating new instances of minority class that is less prone to overfitting than other standard sampling techniques. It is based on a well established theory of nonparametric statistical estimation. Numerical experiments show that KDE can outperform other sampling techniques on a range of real life datasets as measured by F1-score and G-mean. The results remain consistent across a number of classification algorithms used in the experiments. Furthermore, the proposed method outperforms the benchmark methods irregardless of the class distribution ratio. We conclude, based on the solid theoretical foundation and strong experimental results, that the proposed method would be a valuable tool in problems involving imbalanced class distribution.