In clustering-based active learning, the performance of the learner relies heavily on the quality of clustering results. Empirical studies have shown that different clustering techniques are applicable to different data. In this paper, we propose the three-way active learning through clustering selection (TACS) algorithm to dynamically select the appropriate techniques during the learning process. The algorithm follows the coarse-to-fine scheme of granular computing coupled with three-way instance processing. For label query, we select both representative instances with density peaks, and informative instances with the maximal total distance. For block partition, we revise six popular clustering techniques to speed up learning and accommodate binary splitting. For clustering evaluation, we define weighted entropy with 1-nearest-neighbor. For insufficient labels, we design tree pruning techniques with the use of a block queue. Experiments are undertaken on twelve UCI datasets. The results show that TACS is superior to single clustering technique based algorithms and other state-of-the-art active learning algorithms.

中文翻译：

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通过聚类选择进行三向主动学习

在基于聚类的主动学习中，学习者的表现很大程度上取决于聚类结果的质量。实证研究表明，不同的聚类技术适用于不同的数据。本文提出了一种基于聚类选择的三向主动学习（TACS）算法，以在学习过程中动态选择合适的技术。该算法遵循粒度计算的粗到精方案，并结合了三路实例处理。对于标签查询，我们选择具有密度峰值的代表性实例和具有最大总距离的信息实例。对于块分区，我们修订了六种流行的聚类技术，以加快学习速度并适应二进制拆分。对于聚类评估，我们定义了具有1个最近邻居的加权熵。对于标签不足的情况，我们使用块队列设计树修剪技术。在12个UCI数据集上进行了实验。结果表明，TACS优于基于单聚类技术的算法和其他最新的主动学习算法。