| IEICE Technical Committee Submission System Conference Paper's Information |
Online Proceedings [Sign in] Tech. Rep. Archives |
| Go Top Page | Go Previous | [Japanese] / [English] |
| 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 | 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) | |
| Download PDF | IBISML2019-43 |
|
| 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 |
| Keyword(7) | |
| 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) |
| 3rd Author's Name | |
| 3rd Author's Affiliation | () |
| 4th Author's Name | |
| 4th Author's Affiliation | () |
| 5th Author's Name | |
| 5th Author's Affiliation | () |
| 6th Author's Name | |
| 6th Author's Affiliation | () |
| 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 | 8 |
| Date of Issue | 2020-03-03 (IBISML) |
[Return to Top Page]