# Beyond Noise: A Hypothesis Testing Approach to Robust Feature Selection

arXiv: https://arxiv.org/abs/2511.20851 · HTML: https://mlsanity.com/paper/2511.20851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection](https://mlsanity.com/paper/1610.02351/) (2016)
- [Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data](https://mlsanity.com/paper/2007.03788/) (2020)

## Abstract (arXiv)

Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Elimination are either computationally costly or lack a statistically justified stopping criterion for their importance scores. A common heuristic adds random noise features and retains any predictor ranking above the strongest one, but this rule is purely ad hoc. We introduce a method that keeps the noise-augmentation idea while grounding it in theory: each feature's importance is tested against the maximum noise importance using a non-parametric bootstrap hypothesis test, with statistical derivations supporting the algorithm's design. On controlled simulations, the method recovers true signal more consistently than Boruta and Knockoff-based procedures; on diverse real-world datasets, it outperforms Boruta, RFE, and Extra Trees. The result is a robust, principled selector that yields reliable inference, improved prediction, and efficient computation.