Global and local learning from positive and unlabeled examples

Abstract

In common binary classification scenarios, learning algorithms assume the presence of both positive and negative examples. Unfortunately, in many practical areas, only limited labeled positive examples and large amounts of unlabeled examples are available, but there are no negative examples. In such cases, the algorithm that only exploits positive and unlabeled examples is needed. Such learning is termed as positive and unlabeled (PU) learning. In this paper, a novel classifier called global and local learning classifier (GLLC) for PU learning is proposed. The advantages of GLLC are as follows: (1) both intrinsic geometric structure and accurate positive information of PU data are exploited from global learning. (2) The smoothness and manifold of data are reflected sufficiently from local learning. (3) The algorithm of GLLC has faster training speed because the linear equations are solved. (4) The experiments on both synthetic and real datasets verify the above opinions and show that the classification result of GLLC is much better than those popular methods, such as LUHC, Pulce, BSVM, NB and so on.