Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification

• URL: http://arxiv.org/abs/2102.00678v1
• Date: Mon, 1 Feb 2021 07:36:38 GMT
• Title: Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
• Authors: Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama
• Abstract summary: We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
• Score: 94.55805516167369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To cope with high annotation costs, training a classifier only from weakly supervised data has attracted a great deal of attention these days. Among various approaches, strengthening supervision from completely unsupervised classification is a promising direction, which typically employs class priors as the only supervision and trains a binary classifier from unlabeled (U) datasets. While existing risk-consistent methods are theoretically grounded with high flexibility, they can learn only from two U sets. In this paper, we propose a new approach for binary classification from m U-sets for $m\ge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC), which is aimed at predicting from which U set each observed data is drawn. SSC can be solved by a standard (multi-class) classification method, and we use the SSC solution to obtain the final binary classifier through a certain linear-fractional transformation. We built our method in a flexible and efficient end-to-end deep learning framework and prove it to be classifier-consistent. Through experiments, we demonstrate the superiority of our proposed method over state-of-the-art methods.

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