通过顺序测试提前停止，让集成分类器提速4倍以上且误差低于0.1%。
Optimized Sequential Testing for Binary Ensemble Classifiers
- 按顺序逐个评估基模型，发现明显多数时立即停止
- 在0.1%误差控制下实现平均4倍以上的加速
- 适用于对效率敏感的二分类任务
集成分类器通过组合多个基础模型的预测结果提升性能，如随机森林。但模型越多，训练和推理成本越高。本文引入序列检验思想，不强制全部评估所有基模型，而是依次评估并尽早停止——当出现明确多数时即终止。针对三种最优性标准及允许的分歧率，证明可通过线性规划高效求解最优停止策略。在UC Irvine机器学习库及Grinsztajn等人提出的基准数据集上测试，结果显示多数情况下速度提升4倍以上，同时保持分歧率低于0.1%。
原文摘要 · Abstract (English)
Ensemble classifiers are predictive models that combine the results of simpler base models, often by majority vote. A classic example is random forests, which combine the predictions of decision trees. Ensembles that use more base models can be more accurate but also more costly to train and run. In this paper, we consider strategies for reducing the computational cost of binary classification using an approach from the field of sequential testing. Rather than evaluating all the base models and taking a majority vote, we evaluate the base models sequentially and stop execution when a clear majority emerges. We consider three different notions of optimality for early-stopping strategies that minimize the number of base models executed while controlling the rate of disagreement with the full ensemble. For each notion of optimality and allowable disagreement rate, we show that a linear program can be constructed and solved efficiently to find the optimal stopping strategy. We tested these methods on real-world datasets taken from the UC Irvine Machine Learning repository, and on the benchmark datasets proposed by Grinsztajn et al. We found that on most datasets, these methods provide speed-ups of 4x or more while controlling disagreement at 0.1%
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