Abstract

We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. In the presence of such label noise, conventional classification fails to obtain a classifier to be generalized to a population. In this work, we investigate label noise logistic regression and explain how it works with noisy training data. We demonstrate that, when label transition probabilities are correctly provided, label noise logistic regression satisfies the Fisher consistency and enjoys the property of robustness. To accommodate various label noise mechanisms that occur in practice, we propose a flexible label noise model in a nonparametric way. We propose an efficient algorithm under the thresholding rule for individual parameter estimation. We demonstrate its performance under synthetic and real examples. We discuss the proposed flexible transition model is also useful for robust classification.

摘要

在训练数据中的某些观察结果被错误标记的情况下，我们考虑一个二元分类问题。在存在这种标签噪声的情况下，常规分类无法获得可推广到人群的分类器。在这项工作中，我们研究了标签噪声逻辑回归并解释了它如何处理噪声训练数据。我们证明，当正确提供标签转换概率时，标签噪声逻辑回归满足 Fisher 一致性并具有鲁棒性。为了适应实践中出现的各种标签噪声机制，我们以非参数方式提出了一种灵活的标签噪声模型。我们提出了一种在阈值规则下用于个体参数估计的有效算法。我们在合成和真实示例下展示了它的性能。