论文标题
使用基于聚类的积极学习和多样性探索来减轻标记数据的短缺
Mitigating shortage of labeled data using clustering-based active learning with diversity exploration
论文作者
论文摘要
在本文中，我们提出了一个新的基于聚类的主动学习框架，即使用基于聚类的采样（ALC）的主动学习，以解决标记数据的短缺。 ALCS采用基于密度的聚类方法来探索数据集群结构，而无需详尽的参数调整。引入了基于Bi-Cluster边界的样本查询过程，以提高对高度重叠类别分类的学习性能。此外，我们制定了一种有效的多样性探索策略来解决查询样品之间的冗余。我们的实验结果证明了ALCS方法的疗效。
In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data. ALCS employs a density-based clustering approach to explore the cluster structure from the data without requiring exhaustive parameter tuning. A bi-cluster boundary-based sample query procedure is introduced to improve the learning performance for classifying highly overlapped classes. Additionally, we developed an effective diversity exploration strategy to address the redundancy among queried samples. Our experimental results justified the efficacy of the ALCS approach.
