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Paper Abstract and Keywords
Presentation 2021-03-05 11:00
A Model Selection Optimization Method for Distributed Machine Learning with Feature Model Combination
Ryuichi Mochizuki, Takeshi Tsuchiya, Hiroo Hirose, Tetsuyasu Yamada (SUS) IN2020-83
Abstract (in Japanese) (See Japanese page) 
(in English) This study clarifies the optimal feature model selection method in data analysis under the environment where the feature models are distributed among many nodes of a network. Only the effective feature models are selected from a multiple nodes, can be merging to the differences in the characteristics of each model, which are caused by the characteristics of the source data. In the proposed method, the nodes are selected based on the accuracy of data analysis required by the client, synchronizing the information cooperatively among the nodes. In order to evaluate the effectiveness of feature model selection, we compared the task accuracy of training and merging using multiple data sets. In addition, the effectiveness of sequential merging of feature models based on similarity is clarified.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine Learning / Distributed Information Platform / Feature model selection / Feature model merging / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 414, IN2020-83, pp. 172-177, March 2021.
Paper # IN2020-83 
Date of Issue 2021-02-25 (IN) 
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)
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Conference Information
Committee IN NS  
Conference Date 2021-03-04 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To IN 
Conference Code 2021-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Model Selection Optimization Method for Distributed Machine Learning with Feature Model Combination 
Sub Title (in English)  
Keyword(1) Machine Learning  
Keyword(2) Distributed Information Platform  
Keyword(3) Feature model selection  
Keyword(4) Feature model merging  
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1st Author's Name Ryuichi Mochizuki  
1st Author's Affiliation Suwa University of Science (SUS)
2nd Author's Name Takeshi Tsuchiya  
2nd Author's Affiliation Suwa University of Science (SUS)
3rd Author's Name Hiroo Hirose  
3rd Author's Affiliation Suwa University of Science (SUS)
4th Author's Name Tetsuyasu Yamada  
4th Author's Affiliation Suwa University of Science (SUS)
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Speaker Author-1 
Date Time 2021-03-05 11:00:00 
Presentation Time 20 minutes 
Registration for IN 
Paper # IN2020-83 
Volume (vol) vol.120 
Number (no) no.414 
Page pp.172-177 
#Pages 6 
Date of Issue 2021-02-25 (IN) 

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