  During recent years, active learning has evolved into a popular paradigm for
utilizing user's feedback to improve accuracy of learning algorithms. Active
learning works by selecting the most informative sample among unlabeled data
and querying the label of that point from user. Many different methods such as
uncertainty sampling and minimum risk sampling have been utilized to select the
most informative sample in active learning. Although many active learning
algorithms have been proposed so far, most of them work with binary or
multi-class classification problems and therefore can not be applied to
problems in which only samples from one class as well as a set of unlabeled
data are available.
  Such problems arise in many real-world situations and are known as the
problem of learning from positive and unlabeled data. In this paper we propose
an active learning algorithm that can work when only samples of one class as
well as a set of unlabelled data are available. Our method works by separately
estimating probability desnity of positive and unlabeled points and then
computing expected value of informativeness to get rid of a hyper-parameter and
have a better measure of informativeness./ Experiments and empirical analysis
show promising results compared to other similar methods.
