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Binary Classification from Positive-Confidence Data
Takashi Ishida · Gang Niu · Masashi Sugiyama

Thu Dec 02:00 PM -- 04:00 PM PST @ Room 210 #97

Can we learn a binary classifier from only positive data, without any negative data or unlabeled data? We show that if one can equip positive data with confidence (positive-confidence), one can successfully learn a binary classifier, which we name positive-confidence (Pconf) classification. Our work is related to one-class classification which is aimed at "describing" the positive class by clustering-related methods, but one-class classification does not have the ability to tune hyper-parameters and their aim is not on "discriminating" positive and negative classes. For the Pconf classification problem, we provide a simple empirical risk minimization framework that is model-independent and optimization-independent. We theoretically establish the consistency and an estimation error bound, and demonstrate the usefulness of the proposed method for training deep neural networks through experiments.

Author Information

Takashi Ishida (The University of Tokyo, RIKEN, SMAM)
Gang Niu (RIKEN)
Masashi Sugiyama (RIKEN / University of Tokyo)

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