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| Presentation | 2019-12-05 13:50 [Poster Presentation] Quality state analysis of eNodeB log data by semi-supervised learning using Self training Shouta Yoshida (TCU), Atsushi Morohoshi (Fujitsu Fsas), Kohei Shiomoto (TCU), Chin Lam Eng, Sebastian Backstad (Ericsson Japan)  SR2019-92 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In an LTE network where traffic is increasing year by year. It is important to quickly find the cause when a failure occurs in the eNodeB base station. Therefore, using the eNodeB log data extracted from the base station, 13 types of base station states are clasified by machine learning, and high accuracy is achieved while a few labeled data that is expensive to create. In this paper, semi-supervised learning is performed by using self-training, which considers unlabeled data with high confidence as label data, and the accuracy is improved from 86.73％ to 90.13％ compared to normal supervised learning. Also, using two types of methods, Active learning to add labels to data with low confidence, and unlabeled loss function, which is an effective loss function for unlabeled data, Accuracy has improved to 90.96％. The advantage of semi-supervised learning in learning with a few label data was clarified. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | machine learning / semi-supervised learning / Self training / Active learning / unlabeled loss function / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 325, SR2019-92, pp. 29-36, Dec. 2019. | |
| Paper # | SR2019-92 | |
| Date of Issue | 2019-11-28 (SR) | |
| 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 | SR2019-92 |
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| Conference Information | |
| Committee | SR |
| Conference Date | 2019-12-05 - 2019-12-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Ishigaki City Hall (Ishigaki Island) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | cognitive radio, machine learning application, heterogeneous network, SDN, IoT etc. |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2019-12-SR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Quality state analysis of eNodeB log data by semi-supervised learning using Self training |
| Sub Title (in English) | |
| Keyword(1) | machine learning |
| Keyword(2) | semi-supervised learning |
| Keyword(3) | Self training |
| Keyword(4) | Active learning |
| Keyword(5) | unlabeled loss function |
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| Keyword(8) | |
| 1st Author's Name | Shouta Yoshida |
| 1st Author's Affiliation | Tokyo City University (TCU) |
| 2nd Author's Name | Atsushi Morohoshi |
| 2nd Author's Affiliation | Fujitsu Fsas Inc. (Fujitsu Fsas) |
| 3rd Author's Name | Kohei Shiomoto |
| 3rd Author's Affiliation | Tokyo City University (TCU) |
| 4th Author's Name | Chin Lam Eng |
| 4th Author's Affiliation | Ericsson Japan (Ericsson Japan) |
| 5th Author's Name | Sebastian Backstad |
| 5th Author's Affiliation | Ericsson Japan (Ericsson Japan) |
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| Speaker | Author-1 |
| Date Time | 2019-12-05 13:50:00 |
| Presentation Time | 105 minutes |
| Registration for | SR |
| Paper # | SR2019-92 |
| Volume (vol) | vol.119 |
| Number (no) | no.325 |
| Page | pp.29-36 |
| #Pages | 8 |
| Date of Issue | 2019-11-28 (SR) |
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