Learning from Stochastic Labels
This work addresses the time-consuming and laborious task of multi-class annotation for machine learning practitioners, offering a more efficient labeling approach.
The paper tackles the problem of annotating multi-class instances by proposing a novel labeling mechanism called stochastic labels, which reduces annotation effort by allowing correct labels to be identified from a small random subset or marking as None if not present, and demonstrates its superiority through experiments on benchmark datasets.
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.