Outlier Detection Datasets

该数据集研究在低标签条件下，不同的实例选择技术（如UNISEL和主动学习）在10个异常检测数据集上的表现。此外，该数据集还涉及实施k-means聚类来选择实例进行标注，这些标注后的实例随后用于训练随机森林分类器，以执行异常检测任务。

This dataset investigates the performance of various instance selection techniques (e.g., UNISEL and active learning) across 10 anomaly detection datasets under low-label conditions. Additionally, this dataset includes the implementation of k-means clustering to select instances for labeling, and these labeled instances are subsequently used to train a random forest classifier to perform anomaly detection tasks.