Truncated Out-of-Bag Probability Gain: A Noise-Robust Criterion for Random-Forest Wrapper Feature Selection
Read the original on arXiv Machine Learning →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.
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