In multi-label learning, it is rather expensive to label instances since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becomes particularly important for multi-label learning. A good multi-label active learning algorithm usually consists of two crucial elements: a reasonable criterion to evaluate the gain of querying the label for an instance, and an effective classification model, based on whose prediction the criterion can be accurately computed. In this paper, we first introduce an effective multi-label classification model by combining label ranking with threshold learning, which is incrementally trained to avoid retraining from scratch after every query. Based on this model, we then propose to exploit both uncertainty and diversity in the instance space as well as the label space, and actively query the instance-label pairs which can improve the classification model most. Extensive experiments on 20 datasets demonstrate the superiority of the proposed approach to state-of-the-art methods.

中文翻译：

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具有主动查询的增量多标签学习

在多标签学习中，标记实例的成本相当高，因为它们同时与多个标签相关联。因此，通过主动查询最有价值数据的标签来降低标签成本的主动学习对于多标签学习变得尤为重要。一个好的多标签主动学习算法通常由两个关键要素组成：一个合理的标准来评估查询标签对实例的增益，以及一个有效的分类模型，基于其预测可以准确地计算出标准。在本文中，我们首先通过将标签排序与阈值学习相结合来介绍一种有效的多标签分类模型，该模型是增量训练的，以避免在每次查询后从头开始重新训练。基于这个模型，然后，我们建议利用实例空间和标签空间中的不确定性和多样性，并主动查询最能改进分类模型的实例-标签对。对 20 个数据集的大量实验证明了所提出的方法对最先进方法的优越性。