Several algorithms have been developed in the past few years that can learn from a set of labeled positive examples augmented with a set of unlabeled examples.
given a set P of positive examples of a particular class (called the positive class) and a set U of unlabeled examples (which contains both hidden positive and hidden negative examples), a classifier is built using P and U to classify the data in U as well as future test data into two classes, i.e., those belonging to P (positive) and those not belonging to P (negative).