Binary Classification from Positive-Confidence Data
This addresses a data scarcity issue in binary classification for scenarios where only positive examples are available, offering a novel approach compared to traditional one-class methods.
The paper tackles the problem of learning a binary classifier using only positive data with confidence scores, without negative or unlabeled data, and demonstrates its effectiveness through theoretical consistency and experimental results on deep neural networks.
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.