# A model-free feature selection technique of feature screening and random forest based recursive feature elimination

arXiv: https://arxiv.org/abs/2302.07449 · HTML: https://mlsanity.com/paper/2302.07449/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 92 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)
- [Correlation and variable importance in random forests](https://mlsanity.com/paper/1310.5726/) (2013)
- [Forward-Backward Selection with Early Dropping](https://mlsanity.com/paper/1705.10770/) (2017)
- [Model-free Feature Screening and FDR Control with Knockoff Features](https://mlsanity.com/paper/1908.06597/) (2019)
- [The fused Kolmogorov filter: A nonparametric model-free screening method](https://mlsanity.com/paper/1403.7701/) (2014)
- [Consistency of Online Random Forests](https://mlsanity.com/paper/1302.4853/) (2013)
- [Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data](https://mlsanity.com/paper/1308.3942/) (2013)
- [Forward variable selection for sparse ultra-high dimensional varying coefficient models](https://mlsanity.com/paper/1410.6556/) (2014)
- [Asymptotic Unbiasedness of the Permutation Importance Measure in Random Forest Models](https://mlsanity.com/paper/1912.03306/) (2019)
- [A sequential stepwise screening procedure for sparse recovery in high-dimensional multiresponse models with complex group structures](https://mlsanity.com/paper/2208.06567/) (2022)

## Built on by (most cited first, 3 of 3)

- [Binary Feature Mask Optimization for Feature Selection](https://mlsanity.com/paper/2401.12644/) (2024)
- [An explainable Recursive Feature Elimination to detect Advanced Persistent Threats using Random Forest classifier](https://mlsanity.com/paper/2511.09603/) (2025)
- ["FRAME: Forward Recursive Adaptive Model Extraction-A Technique for Advance Feature Selection"](https://mlsanity.com/paper/2501.11972/) (2025)

## Abstract (arXiv)

In this paper, we propose a model-free feature selection method for ultra-high dimensional data with mass features. This is a two phases procedure that we propose to use the fused Kolmogorov filter with the random forest based RFE to remove model limitations and reduce the computational complexity. The method is fully nonparametric and can work with various types of datasets. It has several appealing characteristics, i.e., accuracy, model-free, and computational efficiency, and can be widely used in practical problems, such as multiclass classification, nonparametric regression, and Poisson regression, among others. We show that the proposed method is selection consistent and L2 consistent under weak regularity conditions. We further demonstrate the superior performance of the proposed method over other existing methods by simulations and real data examples.