Multi-label learning studies the problem where one instance is associated with multiple labels. Weakly supervised multi-label learning has attracted considerable research attention because of the annotation difficulty. Majority of the studies on weakly supervised multi-label learning assume that one group of weak annotations is available for each instance; however, none of these studies considers multiple groups of weak annotations that can be easily acquired through crowdsourcing. Recent studies on crowdsourced multi-label learning observed that the current query strategies do not agree well with human habits and that data cannot be collected as expected. Therefore, this study aims to design a new query strategy in accordance with human behavior patterns to obtain multiple groups of weak annotations. Further, a learning algorithm is proposed based on neural networks for such type of data. In addition, this study qualitatively and empirically analyzes factors in the proposed query strategy that may impact further learning and provides insights to obtain better query strategy with respect to future crowdsourcing in case of multi-label data.

多标签学习研究一个实例与多个标签相关联的问题。弱监督的多标签学习由于注释困难而引起了相当大的研究关注。关于弱监督多标签学习的研究多数假设每个实例都可以使用一组弱注释。但是，这些研究都没有考虑可以通过众包轻松获取的弱注释的多组。关于众包多标签学习的最新研究发现，当前的查询策略与人类习惯不太吻合，并且无法按预期收集数据。因此，本研究旨在根据人类行为模式设计一种新的查询策略，以获得多组弱注释。进一步，针对这种类型的数据，提出了一种基于神经网络的学习算法。此外，本研究从定性和实证角度分析了所提出的查询策略中可能影响进阶学习的因素，并提供了见解，以在多标签数据的情况下针对未来的众包获取更好的查询策略。