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Learning from positive and unlabeled data: a survey

Machine learning, 2020-04, Vol.109 (4), p.719-760 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020 ;The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-020-05877-5

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  • Title:
    Learning from positive and unlabeled data: a survey
  • Author: Bekker, Jessa ; Davis, Jesse
  • Subjects: Artificial Intelligence ; Computer Science ; Control ; Knowledge base ; Machine learning ; Mechatronics ; Natural Language Processing (NLP) ; Robotics ; Simulation and Modeling
  • Is Part Of: Machine learning, 2020-04, Vol.109 (4), p.719-760
  • Description: Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0885-6125
    EISSN: 1573-0565
    DOI: 10.1007/s10994-020-05877-5
  • Source: ProQuest Central
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