English(EN) Conditional Inference Trees and Forests for Feature Selection

条件推断森林在特征选择中表现强劲

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-01 19:23

研究人员开发了条件推断树 (CIT) 和条件推断森林 (CIF) 作为机器学习中的特征选择方法。虽然这些方法由于重复的排列检验和阈值搜索可能计算量很大，但该研究证明了它们作为 top-k 特征排序方法的有效性。实验表明，CIF 在现有的分类和回归方法中排名靠前，运行时分析表明自适应停止和搜索的阈值数量对拟合时间有显著影响。 AI

影响引入了一种改进的特征选择方法，该方法在预测基准测试中排名靠前，有可能提高模型效率。

排序理由该集群包含一篇详细介绍机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Robert Milletich, Justin Downes, Steve Goley, Newel Hirst · 2026-07-03 04:00

Conditional Inference Trees and Forests for Feature Selection

arXiv:2607.01417v1 Announce Type: cross 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 metho…
arXiv stat.ML TIER_1 English(EN) · Newel Hirst · 2026-07-01 19:23

Conditional Inference Trees and Forests for Feature Selection

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 CIT and CIF…

报道来源 [2]

Conditional Inference Trees and Forests for Feature Selection

Conditional Inference Trees and Forests for Feature Selection

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