论文标题
关于某些领先分类器和对称损失函数的固有鲁棒性 - 经验评估
On the intrinsic robustness to noise of some leading classifiers and symmetric loss function -- an empirical evaluation
论文作者
论文摘要
在某些工业应用中，例如欺诈检测，常见监督技术的性能可能会受到可用标签质量差的影响：在实际的操作用例中，这些标签的数量，质量或可信度可能很弱。我们提出了一个基准，以评估从人工损坏的数据集中从各种范式中获取的不同算法的自然鲁棒性，重点是嘈杂的标签。本文研究了一些领先的分类器的内在鲁棒性。审查下的算法包括SVM，逻辑回归，随机森林，Xgboost，Khiops。此外，基于最新文献的结果，该研究还接受了对增强具有对称损失函数的算法的机会的研究。
In some industrial applications such as fraud detection, the performance of common supervision techniques may be affected by the poor quality of the available labels : in actual operational use-cases, these labels may be weak in quantity, quality or trustworthiness. We propose a benchmark to evaluate the natural robustness of different algorithms taken from various paradigms on artificially corrupted datasets, with a focus on noisy labels. This paper studies the intrinsic robustness of some leading classifiers. The algorithms under scrutiny include SVM, logistic regression, random forests, XGBoost, Khiops. Furthermore, building on results from recent literature, the study is supplemented with an investigation into the opportunity to enhance some algorithms with symmetric loss functions.
