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The paper introduces TOP‑Gain, a new criterion for Random‑Forest wrapper feature selection that uses truncated out‑of‑bag probability gain to reduce noise in feature scoring. By truncating negative probability changes, TOP‑Gain cuts roughly two‑thirds of noise variance while keeping signal, leading to more reproducible feature rankings. In extensive benchmarks on 26 OpenML‑CC18 datasets, TOP‑Gain outperformed 13 of 14 baselines in ROC‑AUC, accuracy, and Macro‑F1, matched VSURF’s accuracy with far less computation, and produced more stable feature sets without sacrificing accuracy.
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.
arXiv:2607. 01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive.
We study feature bagging through the lens of algorithmic stability. Feature bagging is an ensemble strategy that aggregates base learners trained on randomly subsampled feature subsets, possibly in a data-dependent manner.
arXiv:2607. 26964v1 Announce Type: cross Abstract: We study feature bagging through the lens of algorithmic stability.
arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.