A Modified Logistic Regression for Positive and Unlabeled Learning

Kristen Jaskie, Charles Elkan, Andreas Spanias

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

1 Scopus citations

Abstract

The positive and unlabeled learning problem is a semi-supervised binary classification problem. In PU learning, only an unknown percentage of positive samples are known, while the remaining samples, both positive and negative, are unknown. We wish to learn a decision boundary that separates the positive and negative data distributions. In this paper, we build on an existing popular probabilistic positive unlabeled learning algorithm and introduce a new modified logistic regression learner with a variable upper bound that we argue provides a better theoretical solution for this problem. We then apply this solution to both simulated data and to a simple image classification problem using the MNIST dataset with significantly improved results.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages2007-2011
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period11/3/1911/6/19

Keywords

  • AI
  • PU learning
  • machine learning
  • positive unlabeled learning
  • semi-supervised

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Jaskie, K., Elkan, C., & Spanias, A. (2019). A Modified Logistic Regression for Positive and Unlabeled Learning. In M. B. Matthews (Ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 (pp. 2007-2011). [9048765] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/IEEECONF44664.2019.9048765