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Paper Abstract and Keywords
Presentation 2015-08-05 13:00
Empirical Study on Instance Selection from Healthcare Data Using Fuzzy Rough Sets
Do Van Nguyen (KDDI R&D Labs), Keisuke Ogawa (KDDI), Kazunori Matsumoto, Masayuki Hashimoto (KDDI R&D Labs) DE2015-14
Abstract (in Japanese) (See Japanese page) 
(in English) This research shows a study on issues that cause low performance in disease prediction such as overlap and imbalance and suggests solutions based on fuzzy rough sets to overcome these problems. We apply several methods based on rough set theory to deal with overlap by selecting valuable instances. The considerable results in combination of rough instance selection and balancing technique are then noticed. In this study, we also apply new proposed approach that uses only fuzzy-rough instance selection to dealing with the both issues. This means that without balancing techniques, performance still improves. Some experiments are conducted to show the methods.
Keyword (in Japanese) (See Japanese page) 
(in English) rough set theory / fuzzy-rough sets / healthcare data / classification performance / instance selection / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 177, DE2015-14, pp. 19-24, Aug. 2015.
Paper # DE2015-14 
Date of Issue 2015-07-29 (DE) 
ISSN Print edition: ISSN 0913-5685    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 DE IPSJ-DBS IPSJ-IFAT  
Conference Date 2015-08-05 - 2015-08-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Todaiji Culture Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Management, Information Retrieval, Knowledge Discovery, etc. 
Paper Information
Registration To DE 
Conference Code 2015-08-DE-DBS-IFAT 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Empirical Study on Instance Selection from Healthcare Data Using Fuzzy Rough Sets 
Sub Title (in English)  
Keyword(1) rough set theory  
Keyword(2) fuzzy-rough sets  
Keyword(3) healthcare data  
Keyword(4) classification performance  
Keyword(5) instance selection  
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1st Author's Name Do Van Nguyen  
1st Author's Affiliation KDDI R&D Laboratories Inc. (KDDI R&D Labs)
2nd Author's Name Keisuke Ogawa  
2nd Author's Affiliation KDDI Corporation (KDDI)
3rd Author's Name Kazunori Matsumoto  
3rd Author's Affiliation KDDI R&D Laboratories Inc. (KDDI R&D Labs)
4th Author's Name Masayuki Hashimoto  
4th Author's Affiliation KDDI R&D Laboratories Inc. (KDDI R&D Labs)
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Speaker Author-1 
Date Time 2015-08-05 13:00:00 
Presentation Time 30 minutes 
Registration for DE 
Paper # DE2015-14 
Volume (vol) vol.115 
Number (no) no.177 
Page pp.19-24 
#Pages
Date of Issue 2015-07-29 (DE) 


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