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This paper addresses the computational inefficiency and lack of statistical rigor in existing feature selection methods like Boruta by introducing a non-parametric bootstrap hypothesis test grounded in theoretical derivations. The proposed approach consistently recovers true signals more reliably than current state-of-the-art procedures while maintaining efficient computation across diverse datasets.
Beyond Noise: A Hypothesis Testing Approach to Robust Feature Selection
arXiv:2511.20851v3 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2511.20851 [stat.ML]
(or arXiv:2511.20851v3 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2511.20851 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 230 words
