# Novel GPU Boruta algorithms for feature selection from high-dimensional data

arXiv: https://arxiv.org/abs/2605.09950 · HTML: https://mlsanity.com/paper/2605.09950/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 11 May 2026.

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

- [Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems](https://mlsanity.com/paper/2307.08423/) (2023)
- [A feature selection method based on Shapley values robust to concept shift in regression](https://mlsanity.com/paper/2304.14774/) (2023)
- [Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation](https://mlsanity.com/paper/2504.18566/) (2025)

## Abstract (arXiv)

Most feature selection algorithms, especially wrapper methods, run inefficiently on CPU based platforms because of their high computational complexity. This inefficiency makes them unsuitable for processing large scale datasets. To address this challenge, the present study proposed two GPU accelerated versions of the Boruta feature selection procedure, in which Boruta-Permut relies on permutation based feature importance and Boruta-TreeImp employs importance based on impurity reduction. To evaluate these methods we conducted experiments on both a self constructed dataset and several publicly available datasets. The experimental results show that the proposed GPU accelerated algorithms greatly improve computational efficiency while preserving feature selection accuracy comparable to the original Boruta algorithm. In our analysis we also observe that the impurity reduction based version can overestimate the importance of some features. Overall these findings suggest that performing Boruta feature selection on GPUs offers an effective and cost efficient solution for large scale data analysis, which is a good deal.