Pointwise Binary Classification with Pairwise Confidence Comparisons

Lei Feng*, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

10 Citations (Scopus)

Abstract

To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed. Among them, some consider using pairwise but not pointwise labels, when pointwise labels are not accessible due to privacy, confidentiality, or security reasons. However, as a pairwise label denotes whether or not two data points share a pointwise label, it cannot be easily collected if either point is equally likely to be positive or negative. Thus, in this paper, we propose a novel setting called pairwise comparison (Pcomp) classification, where we have only pairs of unlabeled data that we know one is more likely to be positive than the other. Firstly, we give a Pcomp data generation process, derive an unbiased risk estimator (URE) with theoretical guarantee, and further improve URE using correction functions. Secondly, we link Pcomp classification to noisy-label learning to develop a progressive URE and improve it by imposing consistency regularization. Finally, we demonstrate by experiments the effectiveness of our methods, which suggests Pcomp is a valuable and practically useful type of pairwise supervision besides the pairwise label.
Original languageEnglish
Title of host publicationProceedings of 38th International Conference on Machine Learning (ICML 2021)
EditorsMarina Meila, Tong Zhang
PublisherML Research Press
Pages3252-3262
Number of pages11
Publication statusPublished - 18 Jul 2021
Event38th International Conference on Machine Learning, ICML 2021 - Virtual
Duration: 18 Jul 202124 Jul 2021
https://icml.cc/virtual/2021/index.html
https://icml.cc/Conferences/2021
https://proceedings.mlr.press/v139/

Publication series

NameProceedings of Machine Learning Research
Volume139
ISSN (Print)2640-3498

Conference

Conference38th International Conference on Machine Learning, ICML 2021
Period18/07/2124/07/21
Internet address

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