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Paper Abstract and Keywords
Presentation 2020-03-11 10:45
Calibrated Surrogate Maximization of Linear-Fractional Utility in Binary Classification
Han Bao (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2019-43
Abstract (in Japanese) (See Japanese page) 
(in English) Complex classification performance metrics such as the F-measure and Jaccard index are often used to handle class imbalance. They are not endowed with M-estimation, which makes optimization hard. We consider a family named linear-fractional metrics and propose methods to directly maximize performance objectives via a calibrated surrogate, which is a tractable yet consistent lower-bound of the original objectives.
Keyword (in Japanese) (See Japanese page) 
(in English) binary classification / F-measure / Jaccard index / surrogate loss / classification calibration / calibrated surrogate loss / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 476, IBISML2019-43, pp. 71-78, March 2020.
Paper # IBISML2019-43 
Date of Issue 2020-03-03 (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 2020-03-10 - 2020-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2020-03-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Calibrated Surrogate Maximization of Linear-Fractional Utility in Binary Classification 
Sub Title (in English)  
Keyword(1) binary classification  
Keyword(2) F-measure  
Keyword(3) Jaccard index  
Keyword(4) surrogate loss  
Keyword(5) classification calibration  
Keyword(6) calibrated surrogate loss  
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Keyword(8)  
1st Author's Name Han Bao  
1st Author's Affiliation The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN)
2nd Author's Name Masashi Sugiyama  
2nd Author's Affiliation RIKEN/The University of Tokyo (RIKEN/Univ. of Tokyo)
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Speaker Author-1 
Date Time 2020-03-11 10:45:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2019-43 
Volume (vol) vol.119 
Number (no) no.476 
Page pp.71-78 
#Pages
Date of Issue 2020-03-03 (IBISML) 


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