Alpha-Trimming：面向随机森林的局部自适应树剪枝方法
Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests
AI总结：
针对随机森林中树需完全生长的传统认知局限，提出alpha-trimming快速自适应剪枝算法，可在不重拟合树的下调参，在46个数据集基准测试中有效降低均方预测误差且无大幅上升。
摘要：
我们研究表明，自适应控制随机森林中各回归树的规模能够提升预测性能，这与“树应完全生长”的传统认知相悖。本文提出一种快速剪枝算法alpha-trimming（阿尔法修剪），作为随机森林内树的有效剪枝方法，该方法会在信噪比低的区域执行更激进的剪枝。整体剪枝程度通过调整信息准则惩罚项的权重作为调优参数来控制，标准随机森林是我们alpha-trimmed random forest（阿尔法修剪随机森林）的一个特例。alpha-trimming的一个显著特性是，一旦随机森林中的树完全生长完成，无需重新拟合树即可调整其调优参数。在包含46个示例数据集的基准测试套件中，使用我们的剪枝算法后，均方预测误差通常会大幅降低，且与默认参数设置下使用完全生长树的随机森林相比，误差从未出现大幅上升的情况。
英文摘要：
We demonstrate that adaptively controlling the size of individual regression trees in a random forest can improve predictive performance, contrary to the conventional wisdom that trees should be fully grown. A fast pruning algorithm, alpha-trimming, is proposed as an effective approach to pruning trees within a random forest, where more aggressive pruning is performed in regions with a low signal-to-noise ratio. The amount of overall pruning is controlled by adjusting the weight on an information criterion penalty as a tuning parameter, with the standard random forest being a special case of our alpha-trimmed random forest. A remarkable feature of alpha-trimming is that its tuning parameter can be adjusted without refitting the trees in the random forest once the trees have been fully grown once. In a benchmark suite of 46 example data sets, mean squared prediction error is often substantially lowered by using our pruning algorithm and is never substantially increased compared to a random forest with fully-grown trees at default parameter settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
