# Nonparametric IPSS: Fast, flexible feature selection with false discovery control

arXiv: https://arxiv.org/abs/2410.02208 · HTML: https://mlsanity.com/paper/2410.02208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection](https://mlsanity.com/paper/1610.02351/) (2016)
- [DeepPINK: reproducible feature selection in deep neural networks](https://mlsanity.com/paper/1809.01185/) (2018)
- [Scalable and Efficient Hypothesis Testing with Random Forests](https://mlsanity.com/paper/1904.07830/) (2019)
- [Controlling false discoveries in high-dimensional situations: Boosting with stability selection](https://mlsanity.com/paper/1411.1285/) (2014)
- [Knockoff Boosted Tree for Model-Free Variable Selection](https://mlsanity.com/paper/2002.09032/) (2020)
- [Integrated path stability selection](https://mlsanity.com/paper/2403.15877/) (2024)

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

- [Just add noise: Debiasing tree-based variable importance in mixed data](https://mlsanity.com/paper/2609.14083/) (2026)

## Abstract (arXiv)

Feature selection is a critical task in machine learning and statistics. However, existing feature selection methods either (i) rely on parametric methods such as linear or generalized linear models, (ii) lack theoretical false discovery control, or (iii) identify few true positives. Here, we introduce a general feature selection method with finite-sample false discovery control based on applying integrated path stability selection (IPSS) to arbitrary feature importance scores. The method is nonparametric whenever the importance scores are nonparametric, and it estimates q-values, which are better suited to high-dimensional data than p-values. We focus on two special cases using importance scores from gradient boosting (IPSSGB) and random forests (IPSSRF). Extensive nonlinear simulations with RNA sequencing data show that both methods accurately control the false discovery rate and detect more true positives than existing methods. Both methods are also efficient, running in under 20 seconds when there are 500 samples and 5000 features. We apply IPSSGB and IPSSRF to detect microRNAs and genes related to cancer, finding that they yield better predictions with fewer features than existing approaches.