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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
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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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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)  
Keyword(1) Semi-Supervised Learning  
Keyword(2) PU Learning  
Keyword(3) AUC Optimization  
Keyword(4) 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
Date of Issue 2017-11-02 (IBISML) 


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