Semi-supervised AUC optimization based on positive-unlabeled learning

Semi-supervised AUC optimization based on positive-unlabeled learning Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods have been developed and they are also extended to semi-supervised scenarios to cope with small sample problems. However, existing semi-supervised AUC optimization methods rely on strong distributional assumptions, which are rarely satisfied in real-world problems. In this paper, we propose a novel semi-supervised AUC optimization method that does not require such restrictive assumptions. We first develop an AUC optimization method based only on positive and unlabeled data and then extend it to semi-supervised learning by combining it with a supervised AUC optimization method. We theoretically prove that, without the restrictive distributional assumptions, unlabeled data contribute to improving the generalization performance in PU and semi-supervised AUC optimization methods. Finally, we demonstrate the practical usefulness of the proposed methods through experiments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Learning Springer Journals

Semi-supervised AUC optimization based on positive-unlabeled learning

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Publisher
Springer US
Copyright
Copyright © 2017 by The Author(s)
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Computing Methodologies; Simulation and Modeling; Language Translation and Linguistics
ISSN
0885-6125
eISSN
1573-0565
D.O.I.
10.1007/s10994-017-5678-9
Publisher site
See Article on Publisher Site

Abstract

Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods have been developed and they are also extended to semi-supervised scenarios to cope with small sample problems. However, existing semi-supervised AUC optimization methods rely on strong distributional assumptions, which are rarely satisfied in real-world problems. In this paper, we propose a novel semi-supervised AUC optimization method that does not require such restrictive assumptions. We first develop an AUC optimization method based only on positive and unlabeled data and then extend it to semi-supervised learning by combining it with a supervised AUC optimization method. We theoretically prove that, without the restrictive distributional assumptions, unlabeled data contribute to improving the generalization performance in PU and semi-supervised AUC optimization methods. Finally, we demonstrate the practical usefulness of the proposed methods through experiments.

Journal

Machine LearningSpringer Journals

Published: Oct 26, 2017

References

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