Skip to yearly menu bar Skip to main content


Classification from Positive, Unlabeled and Biased Negative Data

Yu-Guan Hsieh · Gang Niu · Masashi Sugiyama

Pacific Ballroom #180

Keywords: [ Statistical Learning Theory ] [ Semi-supervised learning ] [ Computational Learning Theory ]


In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenarios. However, collecting a non-representative N set that contains only a small portion of all possible N data can often be much easier in practice. This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning. We provide a method based on empirical risk minimization to address this PUbN classification problem. Our approach can be regarded as a novel example-weighting algorithm, with the weight of each example computed through a preliminary step that draws inspiration from PU learning. We also derive an estimation error bound for the proposed method. Experimental results demonstrate the effectiveness of our algorithm in not only PUbN learning scenarios but also ordinary PU learning scenarios on several benchmark datasets.

Live content is unavailable. Log in and register to view live content