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.
By Sejong Oh
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.
By Mousam Sinha, Tirtha Sarathi Ghosh, Koushik Biswas, Ridam Pal
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.
By Robert Milletich, Justin Downes, Steve Goley, Newel Hirst
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.
By Yuheng Ma, Qiang Sun
arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.
By Youngjoon Park
arXiv:2608. 14866v1 Announce Type: cross Abstract: Objective: Small-sample molecular classification requires feature selectors that identify predictive, stable, and nonredundant subsets for binary and multiclass outcomes.
By Zardad Khan, Amjad Ali, Naz Gul, Sheema Gul, Saeed Aldahmani
arXiv:2609.24126v1 Announce Type: cross
Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important fe...
By Xuhui Liu, Lili Zheng
arXiv:2610.33909v1 Announce Type: cross
Abstract: Out-of-distribution (OOD) detection identifies inputs outside the training distribution. Rank-based analyses such as RankFeat and RankWeight suggest...
By Akito Shinohara, Hiroaki Aizawa
arXiv:2608.30086v1 Announce Type: cross
Abstract: Credit-default prediction is an important task in financial decision making. Traditional methods use fitted classifiers such as logistic regression a...
By Rishi Datta, Lavanya Prahallad
JevForest is a feature‑acquisition policy that aggregates path‑dependent proposals from bootstrapped trees, weights them by global training information gain, and uses a shared masked classifier to predict from the acquired values. In online experiments, a four‑question forest on AG News achieved 0.729 accuracy versus 0.667 for a static gain ranking and 0.583 for random ordering, while on TREC the ordering reversed. Offline MiniBooNE tests showed accuracies of 0.845 with ten features and 0.885 with forty features, indicating that Jev’s effectiveness varies with task, predictor, and budget constraints.
By Yu Yan
arXiv:2606. 01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures.
By Amanda S Barnard