Positive unlabeled learning for data stream classification

Learning from positive and unlabeled examples (PU learn-ing) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data is available. An impor-tant challenge is to address the issue of concept drift in the data stream environment, which is not easily handled by the traditional PU learning techniques. This paper studies how to devise PU learning techniques for the data stream envi-ronment. Unlike existing data stream classification methods that assume both positive and negative training data are available for learning, we propose a novel PU learning tech