@inproceedings{fa886c4c7c0545caa14be4789871a57f,
title = "A Modified Logistic Regression for Positive and Unlabeled Learning",
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.",
keywords = "AI, PU learning, machine learning, positive unlabeled learning, semi-supervised",
author = "Kristen Jaskie and Charles Elkan and Andreas Spanias",
note = "Funding Information: This project was funded in part by the ASU SenSIP Center and the NSF I/UCRC award 1540040. Publisher Copyright: {\textcopyright} 2019 IEEE.; 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 ; Conference date: 03-11-2019 Through 06-11-2019",
year = "2019",
month = nov,
doi = "10.1109/IEEECONF44664.2019.9048765",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "2007--2011",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019",
}