研究标签噪声对异常值分类的影响，发现微小噪声也会显著降低模型性能。
Effects of label noise on the classification of outlier observations
- 用校准预测集方法评估异常值分类表现
- 少量噪声使异常值误判率明显上升
- 适合关注模型鲁棒性的研究人员
本研究探讨了在分类任务中向训练集类别添加噪声对BCOPS算法（由Guan & Tibshirani, 2022提出）的影响。BCOPS是一种结合了置信预测与机器学习的方法，旨在构建预测集，使测试样本的真实类别落入预测集的概率满足指定覆盖率。若真实类别未出现在训练集中，则该样本被视为异常值。研究使用合成数据和真实数据集，在此前未被检验的场景下评估了异常值的预测拒识率及模型鲁棒性。结果表明，即使加入极少量噪声，也对模型性能产生显著影响。
原文摘要 · Abstract (English)
This study investigates the impact of adding noise to the training set classes in classification tasks using the BCOPS algorithm (Balanced and Conformal Optimized Prediction Sets), proposed by Guan & Tibshirani (2022). The BCOPS algorithm is an application of conformal prediction combined with a machine learning method to construct prediction sets such that the probability of the true class being included in the prediction set for a test observation meets a specified coverage guarantee. An observation is considered an outlier if its true class is not present in the training set. The study employs both synthetic and real datasets and conducts experiments to evaluate the prediction abstention rate for outlier observations and the model's robustness in this previously untested scenario. The results indicate that the addition of noise, even in small amounts, can have a significant effect on model performance.
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