  Traditional semi-supervised learning uses only labeled instances to train a
classifier and then this classifier is utilized to classify unlabeled
instances, while sometimes there are only positive instances which are elements
of the target concept are available in the labeled set. Our research in this
paper the design of learning algorithms from positive and unlabeled instances
only. Among all the semi-supervised positive and unlabeled learning methods, it
is a fundamental step to extract useful information from unlabeled instances.
In this paper, we design a novel framework to take advantage of valid
information in unlabeled instances. In essence, this framework mainly includes
that (1) selects reliable negative instances through the fuzziness of the
instances; (2) chooses new positive instances based on the fuzziness of the
instances to expand the initial positive set, and we named these new instances
as reliable positive instances; (3) uses data editing technique to filter out
noise points with high fuzziness. The effectiveness of the presented algorithm
is verified by comparative experiments on UCI dataset.
