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

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 210 #97
in Thu Poster Session B »

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.

Keywords: Semi-Supervised Learning

Author Information

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

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