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 |
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) |
Notes on Review |
This article is a technical report without peer review, and its polished version will be published elsewhere. |
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SS2020-35 |
Conference Information |
Committee |
SS |
Conference Date |
2021-03-03 - 2021-03-04 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
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(See Japanese page) |
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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 |
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Machine learning |
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Concept drift |
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Maximum safe radius |
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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 |
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Hitachi, Ltd. (Hitachi) |
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Speaker |
Author-1 |
Date Time |
2021-03-03 15:25:00 |
Presentation Time |
25 minutes |
Registration for |
SS |
Paper # |
SS2020-35 |
Volume (vol) |
vol.120 |
Number (no) |
no.407 |
Page |
pp.43-48 |
#Pages |
6 |
Date of Issue |
2021-02-24 (SS) |
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