在含标签噪声的表格数据上训练用于分类任务的梯度提升决策树
Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks
AI总结：
针对表格分类中的标签噪声问题，研究GBDT鲁棒性，改造两种深度学习噪声检测方法、提出Gradients并扩展重标注方法，在Adult上实现超过99%的检测准确率。
摘要：
标签噪声是指数据集中实例被错误标注的现象，它会显著损害分类器性能、增加模型复杂度并影响特征选择。尽管大多数研究集中于面向图像和文本数据的深度神经网络，本研究探讨标签噪声对梯度提升决策树（GBDT）的影响，后者是处理表格数据的领先算法。本研究通过考察GBDT对标签噪声的鲁棒性填补了空白，重点是将两种源自深度学习的噪声检测方法改造为适用于GBDT，并提出一种名为Gradients的新检测方法。此外，我们扩展了一种最初为GBDT设计的方法，使其纳入重标注功能。通过使用Covertype和Breast Cancer等多样化数据集，我们系统性地引入不同水平的标签噪声，并评估早停和噪声检测方法在维持模型性能方面的有效性。我们的噪声检测方法取得了最先进结果，在Adult数据集上的所有噪声水平下，噪声检测准确率均超过99%。本工作增进了对GBDT中标签噪声的理解，并为未来噪声检测与纠正方法研究提供了基础。
英文摘要：
Label noise, which refers to the mislabeling of instances in a dataset, can significantly impair classifier performance, increase model complexity, and affect feature selection. While most research has concentrated on deep neural networks for image and text data, this study explores the impact of label noise on gradient-boosted decision trees (GBDTs), the leading algorithm for tabular data. This research fills a gap by examining the robustness of GBDTs to label noise, focusing on adapting two noise detection methods from deep learning for use with GBDTs and introducing a new detection method called Gradients. Additionally, we extend a method initially designed for GBDTs to incorporate relabeling. By using diverse datasets such as Covertype and Breast Cancer, we systematically introduce varying levels of label noise and evaluate the effectiveness of early stopping and noise detection methods in maintaining model performance. Our noise detection methods achieve state-of-the-art results, with a noise detection accuracy above 99% on the Adult dataset across all noise levels. This work enhances the understanding of label noise in GBDTs and provides a foundation for future research in noise detection and correction methods.
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。
