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Paper Abstract and Keywords
Presentation 2021-03-03 15:25
Evaluation of Concept Drift Detection by monitoring Maximum Safe Radius
Naoto Sato, Hironobu Kuruma, Hideto Ogawa (Hitachi) SS2020-35
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, machine-learned software is widely used in a system. In machine learning, a model is trained by collected data. If features of the data change during system operation and the changed data is input to the trained model, the accucary of the traind model decreases (it is called concept drift). When the concept drift occurs, the mode accuracy can be recovered by retraining or additionnal training with the drifed data. Thus, to make a system resilient, it is important to detect a decrease in the accuracy and handle it apporpriately as soon as possible. However, to evaluate the accuracy, it is necessary to define expected output data to each input data, and it needs a lot of human costs. Therefore, it would be better if a decrease of the accuracy by the conecpt drift could be detected without expected data. We assume that the maximum safe radius is useful for the concept drift detection. In this report, this assumption is experimentally evaluated. The results show that it is possible to detect a decrease of the accuracy by monitoring maximum safe radius.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Concept drift / Maximum safe radius / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 407, SS2020-35, pp. 43-48, March 2021.
Paper # SS2020-35 
Date of Issue 2021-02-24 (SS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF SS2020-35

Conference Information
Committee SS  
Conference Date 2021-03-03 - 2021-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SS 
Conference Code 2021-03-SS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of Concept Drift Detection by monitoring Maximum Safe Radius 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) Concept drift  
Keyword(3) Maximum safe radius  
1st Author's Name Naoto Sato  
1st Author's Affiliation Hitachi, Ltd. (Hitachi)
2nd Author's Name Hironobu Kuruma  
2nd Author's Affiliation Hitachi, Ltd. (Hitachi)
3rd Author's Name Hideto Ogawa  
3rd Author's Affiliation Hitachi, Ltd. (Hitachi)
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Date Time 2021-03-03 15:25:00 
Presentation Time 25 
Registration for SS 
Paper # IEICE-SS2020-35 
Volume (vol) IEICE-120 
Number (no) no.407 
Page pp.43-48 
#Pages IEICE-6 
Date of Issue IEICE-SS-2021-02-24 

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