  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.
