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Poster
Classification from Positive, Unlabeled and Biased Negative Data
Yu-Guan Hsieh · Gang Niu · Masashi Sugiyama

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #180
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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.

Keywords: Computational Learning Theory · Semi-supervised learning · Statistical Learning Theory

Author Information

Yu-Guan Hsieh (École normale supérieure)
Gang Niu (RIKEN)
Gang Niu

Gang Niu is currently an indefinite-term research scientist at RIKEN Center for Advanced Intelligence Project.

Masashi Sugiyama (RIKEN / The University of Tokyo)

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