English(EN) Truncated Out-of-Bag Probability Gain: A Noise-Robust Criterion for Random-Forest Wrapper Feature Selection

新的TOP-Gain方法提高了随机森林特征选择的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:35

研究人员推出了一种新颖的随机森林包装器特征选择标准TOP-Gain，旨在减轻袋外预测中的噪声。该方法将概率增益的下降截断为零，在26个OpenML-CC18数据集的基准研究中表现出优越的性能。TOP-Gain在ROC-AUC、准确率和Macro-F1的平均排名中均取得最佳成绩，优于14个基线中的13个，同时与VSURF相比显著降低了计算成本。 AI

影响提高了机器学习模型中特征选择的效率和鲁棒性。

排序理由该集群描述了一种用于随机森林的新特征选择方法，该方法在研究论文中有详细介绍。 [lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-21 04:35

截断袋外概率增益：一种用于随机森林包装器特征选择的噪声鲁棒标准

Random-Forest wrapper feature selection can use out-of-bag (OOB) predictions as a built-in validation signal, but the criteria that decide each forward step accumulate OOB estimation noise. We propose TOP-Gain (Truncated Out-of-bag Probability Gain), which scores a candidate feat…

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报道来源 [1]

截断袋外概率增益：一种用于随机森林包装器特征选择的噪声鲁棒标准

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