Semisupervised Prior Free Rare Category Detection With Mixed Criteria
提出一种结合主动学习和半监督层次密度聚类的稀有类别检测框架，无需预先知道类别数量，能处理非线性映射和复杂边界，在真实和合成数据集上表现更优。
Rare category detection aims to find interesting and statistically significant anomalies and incorporates ideas from active learning and semisupervised learning. The challenge of rare category detection is to find the rare classes of the anomalies in a data set where the data distribution is skewed. Most existing rare category detection methods suppose that the user knows the specific number of all classes in advance, which cannot be satisfied in most real scenarios. In this paper, we propose a new rare category detection framework composed of active learning and semisupervised hierarchical density-based clustering. The advantage of our method is that it is prior free and can benefit the rare category detecting process with the labeled data. In addition, the proposed framework can handle tasks with nonlinear mappings, which increases the ability to find rare classes when the class boundary is sophisticated. Compared to existing methods, better results are achieved by our method on both real and synthetic data sets in the experiment.