Positive and Unlabeled Learning Algorithms and Applications: A Survey

Kristen Jaskie, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

This paper will address the Positive and Unlabeled learning problem (PU learning) and its importance in the growing field of semi-supervised learning. In most real-world classification applications, well labeled data is expensive or impossible to obtain. We can often label a small subset of data as belonging to the class of interest. It is frequently impractical to manually label all data we are not interested in. We are left with a small set of positive labeled items of interest and a large set of unknown and unlabeled data. Learning a model for this is the PU learning problem.In this paper, we explore several applications for PU learning including examples in biological/medical, business, security, and signal processing. We then survey the literature for new and existing solutions to the PU learning problem.

Original languageEnglish (US)
Title of host publication10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728149592
DOIs
StatePublished - Jul 2019
Event10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 - Patras, Greece
Duration: Jul 15 2019Jul 17 2019

Publication series

Name10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

Conference

Conference10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
Country/TerritoryGreece
CityPatras
Period7/15/197/17/19

Keywords

  • Artificial intelligence
  • Classification
  • Machine learning
  • PU learning
  • Positive unlabeled learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Media Technology
  • Education

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