Instance- and label-dependent label noise (ILN) is widely existed in real-world datasets but has been rarely studied. In this paper, we focus on a particular case of ILN where the label noise rates, representing the probabilities that the true labels of examples flip into the corrupted labels, have upper bounds. We propose to handle this bounded instance- and label-dependent label noise under two different conditions. First, theoretically, we prove that when the marginal distributions $P(X|Y=+1)$ and $P(X|Y=-1)$ have non-overlapping supports, we can recover every noisy example's true label and perform supervised learning directly on the cleansed examples. Second, for the overlapping situation, we propose a novel approach to learn a well-performing classifier which needs only a few noisy examples to be labeled manually. Experimental results demonstrate that our method works well on both synthetic and real-world datasets.

提出了一种学习算法，该算法能够识别和纠正具有上限的实例和标签依赖性标签噪声（BILN），并证明了在某些条件下，学习基于精简实例的分类器将收敛于贝叶斯最优分类器，该算法在合成和真实数据集上进行了实证评估，并证明了其对BILN的鲁棒性和有效性。

受界实例和标签相关标签噪声的学习