Active Learning by Querying Informative and Representative Examples
Active Learning by Querying Informative and Representative Examples
复制标题
通过查询信息丰富且具有代表性的示例进行主动学习
DOI：
10.1109/tpami.2014.2307881
复制
发表时间：
2014-10-01
影响因子：
23.6
通讯作者：
Zhou, Zhi-Hua
中科院分区：
文献类型：
--
作者：
Huang, Sheng-Jun;Jin, Rong;Zhou, Zhi-Hua
主动学习通过迭代地选择最有价值的数据来查询它们的标签来降低标签成本。由于大量的未标记数据和高昂的标记成本，它吸引了很多人的兴趣。大多数主动学习方法选择信息或代表性的未标记的实例来查询它们的标签，这可能会显着限制它们的性能。尽管提出了几种主动学习算法来联合收割机结合这两个查询选择标准，但它们通常是临时的，用于寻找既有信息又有代表性的未标记实例。我们通过开发一种原则性的方法来解决这个限制，称为QUIRE，基于主动学习的最小-最大观点。所提出的方法提供了一个系统的方法来测量和结合的信息性和代表性的一个未标记的实例。此外，通过将标签之间的相关性，我们扩展了QUIRE方法，通过主动查询实例标签对多标签学习。大量的实验结果表明，所提出的QUIRE方法优于几个国家的最先进的主动学习方法在单标签和多标签学习。
Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.