Transfer Anomaly Detection for Maximizing the Partial AUC

The partial area under the receiver operating characteristic curve (pAUC) is a useful performance measurement for binary classification that summarizes true positive rates (TPRs) within a specific range of false positive rates (FPRs). Obtaining anomaly detectors that achieve high pAUCs is important in practice. Although several methods have been proposed for maximizing the pAUC, existing methods require both anomalous and normal instances for training. However, anomalous instances are difficult to collect due to their rarity. In this paper, we propose a method to maximize the pAUC on target tasks, in which only normal instances are available. To alleviate the absence of anomalies, the proposed method transfers useful knowledge of both anomalous and normal instances in multiple source tasks to the target tasks. Our model first embeds each instance into a task-specific latent space created from normal instances in a task using neural networks. Then, the anomaly score for each instance is obtained by a FPR range-specific linear projection, which is modeled by a neural network that tasks a FPR range as input. By this modeling, we can achieve high pAUCs within any FPR range given target normal instances without re-training. Our model is trained by maximizing the expected test pAUC within any FPR range given normal training instances using anomalous and normal instances in source tasks. We experimentally demonstrate that the proposed method outperforms various existing methods.