Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification

TL;DR

This paper introduces a novel method for binary classification using multiple unlabeled datasets by employing surrogate set classification, enabling effective learning beyond two datasets and improving over existing methods.

Contribution

It extends risk-consistent binary classification to multiple unlabeled datasets using surrogate set classification within a deep learning framework.

Findings

Outperforms state-of-the-art methods in experiments.

Proves classifier consistency theoretically.

Efficient end-to-end deep learning implementation.

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.