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Paper Abstract and Keywords
Presentation 2011-11-21 13:00
An Imputation of context data by using Random Forest
Tsunenori Ishioka (NCUEE) AI2011-21
Abstract (in Japanese) (See Japanese page) 
(in English) When considering contextware services, we set the response variable to the services to provide, and explanatory variable to the context information statistics. Usually, the explanatory variables contain some missing data. It is obvious that missing at random (MAR), which the missing depends on only observations not non-observations, is superior to missing completely at random (MCAR), which the missing does not depend on the variables in an assumed model. Random Forest (RF) is subject to the assumption of MAR, so it derive the better results than those by other conventional methods. The RF imputation can be activated since the Version 4. While being aware that RF is an ensemble learning method for the classification and/or non-linear regressions, many statistician and
engineers do not know the availability of the missing data imputation. In this paper, we present the RF imputation algorithm, indicating that it works pretty well by comparing to kernel method on support vector machines.
Keyword (in Japanese) (See Japanese page) 
(in English) ensemble learning / Random Forest / data imputation / missing data / MAR / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 310, AI2011-21, pp. 25-30, Nov. 2011.
Paper # AI2011-21 
Date of Issue 2011-11-14 (AI) 
ISSN Print edition: ISSN 0913-5685    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 AI2011-21

Conference Information
Committee AI  
Conference Date 2011-11-21 - 2011-11-21 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To AI 
Conference Code 2011-11-AI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Imputation of context data by using Random Forest 
Sub Title (in English)  
Keyword(1) ensemble learning  
Keyword(2) Random Forest  
Keyword(3) data imputation  
Keyword(4) missing data  
Keyword(5) MAR  
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1st Author's Name Tsunenori Ishioka  
1st Author's Affiliation National Center for University Entrance Examinations (NCUEE)
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Speaker Author-1 
Date Time 2011-11-21 13:00:00 
Presentation Time 30 minutes 
Registration for AI 
Paper # AI2011-21 
Volume (vol) vol.111 
Number (no) no.310 
Page pp.25-30 
#Pages 6 
Date of Issue 2011-11-14 (AI) 

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