Gong Chen, Zulfiqar Muhammad Imran, Zhang Chuang, Mahmood Shahid, Yang Jian
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
PCA Lab, the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information, Ministry of Education, Nanjing 210094, China.
Fundam Res. 2022 Oct 12;5(2):796-803. doi: 10.1016/j.fmre.2022.09.019. eCollection 2025 Mar.
Training with confident positive-labeled instances has received a lot of attention in Positive and Unlabeled (PU) learning tasks, and this is formally termed "Instance-Dependent PU learning". In instance-dependent PU learning, whether a positive instance is labeled depends on its labeling confidence. In other words, it is assumed that not all positive instances have the same probability to be included by the positive set. Instead, the instances that are far from the potential decision boundary are with larger probability to be labeled than those that are close to the decision boundary. This setting has practical importance in many real-world applications such as medical diagnosis, outlier detection, object detection, etc. In this survey, we first present the preliminary knowledge of PU learning, and then review the representative instance-dependent PU learning settings and methods. After that, we thoroughly compare them with typical PU learning methods on various benchmark datasets and analyze their performances. Finally, we discuss the potential directions for future research.
在正例和无标签（PU）学习任务中，使用有置信度的正标签实例进行训练受到了广泛关注，这被正式称为“依赖实例的PU学习”。在依赖实例的PU学习中，一个正例是否被标记取决于其标记置信度。换句话说，假设并非所有正例被包含在正例集中的概率都相同。相反，远离潜在决策边界的实例被标记的概率比接近决策边界的实例更大。这种设置在许多实际应用中具有实际重要性，如医学诊断、异常检测、目标检测等。在本综述中，我们首先介绍PU学习的初步知识，然后回顾具有代表性的依赖实例的PU学习设置和方法。之后，我们在各种基准数据集上对它们与典型的PU学习方法进行了全面比较，并分析了它们的性能。最后，我们讨论了未来研究的潜在方向。
