Annotating multi-class instances is a crucial task in the field of machine learning. Unfortunately, identifying the correct class label from a long sequence of candidate labels is time-consuming and laborious. To alleviate this problem, we design a novel labeling mechanism called stochastic label. In this setting, stochastic label includes two cases: 1) identify a correct class label from a small number of randomly given labels; 2) annotate the instance with None label when given labels do not contain correct class label. In this paper, we propose a novel suitable approach to learn from these stochastic labels. We obtain an unbiased estimator that utilizes less supervised information in stochastic labels to train a multi-class classifier. Additionally, it is theoretically justifiable by deriving the estimation error bound of the proposed method. Finally, we conduct extensive experiments on widely-used benchmark datasets to validate the superiority of our method by comparing it with existing state-of-the-art methods.
翻译:在机器学习领域,批注多类实例是一项关键的任务。 不幸的是,从一长串候选标签中识别正确的类标签既费时又费力。 为了缓解这个问题,我们设计了一个叫“随机标签”的新标签机制。在这一背景下,随机标签包括两个实例:(1) 从少量随机贴标签中识别正确的类标签;(2) 在给定标签不包含正确的类标签时,批注无类标签的例。在本文中,我们提出了一个新颖的合适方法,从这些随机标签中学习。我们得到了一个利用不那么受监督的分类标签中的信息来培训多类分类分类师的公正估算师。此外,从理论上讲,通过推断拟议方法的误差是有道理的。最后,我们对广泛使用的基准数据集进行了广泛的实验,以验证我们方法的优越性,将它与现有的最新方法进行比较。