Paper Abstract and Keywords |
Presentation |
2017-11-09 13:00
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-40 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
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 (PU-AUC) 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. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Semi-Supervised Learning / PU Learning / AUC Optimization / Classification / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-40, pp. 39-46, Nov. 2017. |
Paper # |
IBISML2017-40 |
Date of Issue |
2017-11-02 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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IBISML2017-40 |
Conference Information |
Committee |
IBISML |
Conference Date |
2017-11-08 - 2017-11-10 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Univ. of Tokyo |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Information-Based Induction Science Workshop (IBIS2017) |
Paper Information |
Registration To |
IBISML |
Conference Code |
2017-11-IBISML |
Language |
English (Japanese title is available) |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning |
Sub Title (in English) |
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Keyword(1) |
Semi-Supervised Learning |
Keyword(2) |
PU Learning |
Keyword(3) |
AUC Optimization |
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Classification |
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1st Author's Name |
Tomoya Sakai |
1st Author's Affiliation |
The University of Tokyo/RIKEN (UTokyo/RIKEN) |
2nd Author's Name |
Gang Niu |
2nd Author's Affiliation |
The University of Tokyo/RIKEN (UTokyo/RIKEN) |
3rd Author's Name |
Masashi Sugiyama |
3rd Author's Affiliation |
RIKEN/The University of Tokyo (RIKEN/UTokyo) |
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Speaker |
Author-1 |
Date Time |
2017-11-09 13:00:00 |
Presentation Time |
150 minutes |
Registration for |
IBISML |
Paper # |
IBISML2017-40 |
Volume (vol) |
vol.117 |
Number (no) |
no.293 |
Page |
pp.39-46 |
#Pages |
8 |
Date of Issue |
2017-11-02 (IBISML) |
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