通过优化阈值自动选择关键特征，大幅减少数据量与计算时间。
FeatureCuts: Feature Selection for Large Data by Optimizing the Cutoff
- 先过滤排序再动态选阈值，自适应确定最优特征子集。
- 平均多删15个百分点特征，计算时间减少99.6%且性能不变。
- 适合企业级大数据场景，对后续优化算法也有显著加速作用。
在机器学习中，特征选择旨在找出能充分支持高效准确模型训练的最小特征子集。本文提出 FeatureCuts，一种新型特征选择算法，在完成过滤排序后自适应地确定最优特征阈值。在14个公开数据集和一个工业数据集上评估，相比现有最先进方法，FeatureCuts 平均实现15个百分点的特征缩减，计算时间最多降低99.6%，同时保持模型性能。当所选特征用于粒子群优化（PSO）等包装法时，可实现25个百分点的额外特征缩减，计算时间减少66%，且性能不受影响。FeatureCuts 开销极小，适用于企业级大规模数据场景。
原文摘要 · Abstract (English)
In machine learning, the process of feature selection involves finding a reduced subset of features that captures most of the information required to train an accurate and efficient model. This work presents FeatureCuts, a novel feature selection algorithm that adaptively selects the optimal feature cutoff after performing filter ranking. Evaluated on 14 publicly available datasets and one industry dataset, FeatureCuts achieved, on average, 15 percentage points more feature reduction and up to 99.6% less computation time while maintaining model performance, compared to existing state-of-the-art methods. When the selected features are used in a wrapper method such as Particle Swarm Optimization (PSO), it enables 25 percentage points more feature reduction, requires 66% less computation time, and maintains model performance when compared to PSO alone. The minimal overhead of FeatureCuts makes it scalable for large datasets typically seen in enterprise applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。