Multi-Label Active Learning from Crowds
This work addresses label cost reduction for multi-label classification tasks in crowdsourcing settings, representing an incremental improvement by adapting active learning to imperfect annotators.
The paper tackles the problem of reducing labeling costs in multi-label classification by using crowdsourced annotators instead of a single oracle, proposing a method that selects both valuable instances and reliable annotators, with experimental results showing its effectiveness.
Multi-label active learning is a hot topic in reducing the label cost by optimally choosing the most valuable instance to query its label from an oracle. In this paper, we consider the poolbased multi-label active learning under the crowdsourcing setting, where during the active query process, instead of resorting to a high cost oracle for the ground-truth, multiple low cost imperfect annotators with various expertise are available for labeling. To deal with this problem, we propose the MAC (Multi-label Active learning from Crowds) approach which incorporate the local influence of label correlations to build a probabilistic model over the multi-label classifier and annotators. Based on this model, we can estimate the labels for instances as well as the expertise of each annotator. Then we propose the instance selection and annotator selection criteria that consider the uncertainty/diversity of instances and the reliability of annotators, such that the most reliable annotator will be queried for the most valuable instances. Experimental results demonstrate the effectiveness of the proposed approach.