Multi-Task Learning with Positive and Unlabeled Data and Its Application to Mental State Prediction

In real-world machine learning applications, we are often faced with a situation where only a small number of training samples is available due to high sampling costs. For instance, prediction of mental states such as drowsiness from physiological information is a typical example. To cope with this problem, classifier training methods only from positive and unlabeled data and multi-task learning methods for improving the classification performance by solving multiple related tasks simultaneously have been actively investigated recently. In this paper, we combine these methods and propose a multitask learning method that can handle positive-unlabeled tasks and positive-negative tasks in a unified manner. Through experiments on drivers' drowsiness prediction, we demonstrate the effectiveness of the proposed method.