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Paper Abstract and Keywords
Presentation 2016-11-16 15:00
Selective Inference for High-Dimensional Binary Classification
Yuta Umezu, Kazuya Nakagawa (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) IBISML2016-59
Abstract (in Japanese) (See Japanese page) 
(in English) In machine learning and other related area, the number of features is often reduced by some feature selection procedure as a pre-processing for data analysis when it is large. However, once features are selected from the data via some algorithm, we need to correct a selection bias for a statistical inference after feature selection. A selective inference is a kind of statistical inference for correcting the selection bias, and several results are developed until now. Almost results, however, require the normality of the response, so we can not apply it for other important tasks such as classification. In this paper, we extend the selective inference for classification problem based on the high dimensional asymptotic theory. Through some simulation studies, we check the performance of our proposal.
Keyword (in Japanese) (See Japanese page) 
(in English) Family-wise Error Rate / High Dimensional Asymptotics / Hypothesis Testing / Selective Inference / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-59, pp. 93-100, Nov. 2016.
Paper # IBISML2016-59 
Date of Issue 2016-11-09 (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 IBISML2016-59

Conference Information
Committee IBISML  
Conference Date 2016-11-16 - 2016-11-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2016) 
Paper Information
Registration To IBISML 
Conference Code 2016-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Selective Inference for High-Dimensional Binary Classification 
Sub Title (in English)  
Keyword(1) Family-wise Error Rate  
Keyword(2) High Dimensional Asymptotics  
Keyword(3) Hypothesis Testing  
Keyword(4) Selective Inference  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Yuta Umezu  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Kazuya Nakagawa  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Koji Tsuda  
3rd Author's Affiliation University of Tokyo (Univ. of Tokyo)
4th Author's Name Ichiro Takeuchi  
4th Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2016-11-16 15:00:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2016-59 
Volume (vol) vol.116 
Number (no) no.300 
Page pp.93-100 
#Pages 8 
Date of Issue 2016-11-09 (IBISML) 

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