Partial AUC Maximization via Nonlinear Scoring Functions
This work addresses the need for better pAUC optimization in applications with biased data, such as anomaly detection and diagnostic testing, though it is incremental as it builds on existing pAUC methods by adding nonlinearity.
The authors tackled the problem of directly maximizing partial AUC (pAUC) for binary classification in biased scenarios like anomaly detection by introducing nonlinear scoring functions based on generative models and deep neural networks, showing experimental improvements over conventional linear methods in a real-world astronomy dataset.
We propose a method for maximizing a partial area under a receiver operating characteristic (ROC) curve (pAUC) for binary classification tasks. In binary classification tasks, accuracy is the most commonly used as a measure of classifier performance. In some applications such as anomaly detection and diagnostic testing, accuracy is not an appropriate measure since prior probabilties are often greatly biased. Although in such cases the pAUC has been utilized as a performance measure, few methods have been proposed for directly maximizing the pAUC. This optimization is achieved by using a scoring function. The conventional approach utilizes a linear function as the scoring function. In contrast we newly introduce nonlinear scoring functions for this purpose. Specifically, we present two types of nonlinear scoring functions based on generative models and deep neural networks. We show experimentally that nonlinear scoring fucntions improve the conventional methods through the application of a binary classification of real and bogus objects obtained with the Hyper Suprime-Cam on the Subaru telescope.