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Paper Abstract and Keywords
Presentation 2010-06-14 17:10
Regularized Random Forest Method for Survival Analysis
Toshio Shimokawa (Yamanashi Univ.), Mitsuhiro Tsuji (Kansai Univ.) IBISML2010-12
Abstract (in Japanese) (See Japanese page) 
(in English) One of the important themes in survival analysis is to explore prognoses factors that influence survival time. Recently, the tree-structured method has been applied to evaluate covariates (e.g., Crowley, 2004); however, it is well known that this method has provides poor prediction model. This problem could be improved by modeling many trees in a linear combination, namely, ensemble learning. The ensemble learning method is actively studied in machine learning and statistics. The random forest is popular method that is applied in many fields (e.g., bioinformatics, environmentrics, and so on).

In this presentation, we extended the random forest method to analyze survival data. Our proposed model has weight parameters, which are estimated by lasso (Tibshirani, 1996), for each tree. We call this method regurarized random survival forest method. Therefore, in regurarized random survival forest method, the trees (base learner) that strongly influence survival time will have large estimated parameter values; the parameters of trees that lack influence will be estimated as zero (pruning). Evaluation of regurarized random forest method, using simulated and real data sets, indicated that regurarized random survival forest method performs better than the ordinaly random survival forest method.
Keyword (in Japanese) (See Japanese page) 
(in English) random forest / lasso / proportional hazard model / survival analysis / / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 76, IBISML2010-12, pp. 71-77, June 2010.
Paper # IBISML2010-12 
Date of Issue 2010-06-07 (IBISML) 
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 IBISML2010-12

Conference Information
Committee IBISML  
Conference Date 2010-06-14 - 2010-06-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Takeda Hall, Univ. Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2010-06-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Regularized Random Forest Method for Survival Analysis 
Sub Title (in English)  
Keyword(1) random forest  
Keyword(2) lasso  
Keyword(3) proportional hazard model  
Keyword(4) survival analysis  
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1st Author's Name Toshio Shimokawa  
1st Author's Affiliation University of Yamanashi (Yamanashi Univ.)
2nd Author's Name Mitsuhiro Tsuji  
2nd Author's Affiliation Kansai University (Kansai Univ.)
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Speaker Author-1 
Date Time 2010-06-14 17:10:00 
Presentation Time 15 minutes 
Registration for IBISML 
Paper # IBISML2010-12 
Volume (vol) vol.110 
Number (no) no.76 
Page pp.71-77 
#Pages 7 
Date of Issue 2010-06-07 (IBISML) 

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