# The Kernel Density Integral Transformation

arXiv: https://arxiv.org/abs/2309.10194 · HTML: https://mlsanity.com/paper/2309.10194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [An Analysis of Quantile Temporal-Difference Learning](https://mlsanity.com/paper/2301.04462/) (2023)
- [Conformalized Fairness via Quantile Regression](https://mlsanity.com/paper/2210.02015/) (2022)
- [DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering](https://mlsanity.com/paper/1307.8136/) (2013)
- [Quantile-based bias correction and uncertainty quantification of extreme event attribution statements](https://mlsanity.com/paper/1602.04139/) (2016)

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

- [Unmasking Trees for Tabular Data](https://mlsanity.com/paper/2407.05593/) (2024)
- [Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks](https://mlsanity.com/paper/2310.14720/) (2023)

## Abstract (arXiv)

Feature preprocessing continues to play a critical role when applying machine learning and statistical methods to tabular data. In this paper, we propose the use of the kernel density integral transformation as a feature preprocessing step. Our approach subsumes the two leading feature preprocessing methods as limiting cases: linear min-max scaling and quantile transformation. We demonstrate that, without hyperparameter tuning, the kernel density integral transformation can be used as a simple drop-in replacement for either method, offering protection from the weaknesses of each. Alternatively, with tuning of a single continuous hyperparameter, we frequently outperform both of these methods. Finally, we show that the kernel density transformation can be profitably applied to statistical data analysis, particularly in correlation analysis and univariate clustering.