  Existing algorithms aiming to learn a binary classifier from positive (P) and
unlabeled (U) data generally require estimating the class prior or label noises
ahead of building a classification model. However, the estimation and
classifier learning are normally conducted in a pipeline instead of being
jointly optimized. In this paper, we propose to alternatively train the two
steps using reinforcement learning. Our proposal adopts a policy network to
adaptively make assumptions on the labels of unlabeled data, while a classifier
is built upon the output of the policy network and provides rewards to learn a
better strategy. The dynamic and interactive training between the policy maker
and the classifier can exploit the unlabeled data in a more effective manner
and yield a significant improvement on the classification performance.
Furthermore, we present two different approaches to represent the actions
sampled from the policy. The first approach considers continuous actions as
soft labels, while the other uses discrete actions as hard assignment of labels
for unlabeled examples.We validate the effectiveness of the proposed method on
two benchmark datasets as well as one e-commerce dataset. The result shows the
proposed method is able to consistently outperform state-of-the-art methods in
various settings.
