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Paper Abstract and Keywords
Presentation 2019-01-17 10:05
Study on Extraction of Important Data Based on Feature Selection Ensemble for Real-time Predictive Information Delivery
Takumi Sakai, Ryoichi Shinkuma, Yuichi Inagaki, Takehiro Sato, Eiji Oki (Kyoto Univ) MoNA2018-67
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, the demands on the services that predict and deliver real-time spatial information, such as road-traffic volume, have been increasing.
Mobile IoT devices that collect data for these services cannot necessarily transmit all of these data because of the bandwidth limitation in mobile networks.
Some previous works have reduced the volume of data transmission while maintaining prediction accuracy by prioritizing data transmission based on data importance.
They used feature selection methods to extract data importance from the machine learning model for the prediction.
However, some feature selection methods may deteriorate the prediction accuracy because what feature selection method achieves the best prediction accuracy depends on what dataset is used.
Therefore, this work proposes a method to ensemble data importance extracted by multiple feature selection methods to reduce the deterioration of the prediction accuracy.
Evaluation with real-world datasets shows that the proposed system suppresses the deterioration of the prediction accuracy by using ensembled data importance.
Keyword (in Japanese) (See Japanese page) 
(in English) feature selection ensemble / IoT / machine learning / real-time prediction / priority control / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 389, MoNA2018-67, pp. 57-61, Jan. 2019.
Paper # MoNA2018-67 
Date of Issue 2019-01-09 (MoNA) 
ISSN 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 MoNA  
Conference Date 2019-01-16 - 2019-01-17 
Place (in Japanese) (See Japanese page) 
Place (in English) T. B. D. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MoNA 
Conference Code 2019-01-MoNA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Extraction of Important Data Based on Feature Selection Ensemble for Real-time Predictive Information Delivery 
Sub Title (in English)  
Keyword(1) feature selection ensemble  
Keyword(2) IoT  
Keyword(3) machine learning  
Keyword(4) real-time prediction  
Keyword(5) priority control  
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1st Author's Name Takumi Sakai  
1st Author's Affiliation Kyoto University (Kyoto Univ)
2nd Author's Name Ryoichi Shinkuma  
2nd Author's Affiliation Kyoto University (Kyoto Univ)
3rd Author's Name Yuichi Inagaki  
3rd Author's Affiliation Kyoto University (Kyoto Univ)
4th Author's Name Takehiro Sato  
4th Author's Affiliation Kyoto University (Kyoto Univ)
5th Author's Name Eiji Oki  
5th Author's Affiliation Kyoto University (Kyoto Univ)
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Speaker Author-1 
Date Time 2019-01-17 10:05:00 
Presentation Time 25 minutes 
Registration for MoNA 
Paper # MoNA2018-67 
Volume (vol) vol.118 
Number (no) no.389 
Page pp.57-61 
#Pages
Date of Issue 2019-01-09 (MoNA) 


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