Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation

Labeled data are often scarce and expensive to obtain in many real-world applications, making training machine-learning models a challenging task [1]. In traditional supervised learning, the goal is to train a model to predict the correct class label for every sample in a training dataset [2]. The training data consist of labeled examples associated with a known class label. Typically, the class distribution of labeled data is assumed to be representative of the class distribution of unlabeled ones. Prior knowledge of the labels makes it easy to train a model to accurately predict the class labels for unseen samples. As Figure 1 shows, in the case of PU learning, the class label is known only for data belonging to a single class; thus, for negative samples, the label is unknown [3]. The lack of this knowledge makes it impossible to effectively train a typical binary classification model to distinguish between positive and negative classes.