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Paper Abstract and Keywords
Presentation 2024-06-20 15:00
Selective Inference for Reliability Quantification of k-Nearest Neighbor Anomoaly Detection
Mizuki Niihori (Nagoya Univ.), Masaya Ikuta (NITech), Akihumi Yamada, Kouichi Taji, Ichiro Takeuchi (Nagoya Univ.) NC2024-8 IBISML2024-8
Abstract (in Japanese) (See Japanese page) 
(in English) Currently, the $k$-nearest neighbor method is widely used in the field of machine learning anomaly detection.
This method is applicable to a variety of data because it does not assume a distribution of the data, but a quantitative evaluation method for the reliability of the results of anomaly detection has not been established.
In general, statistical hypothesis testing is known as a quantitative evaluation method for the reliability of results. However, in data-driven science, classical statistical hypothesis testing is not adequate for reliability evaluation because hypotheses are selected based on data.
In this study, we use the Selective Inference framework for anomaly detection results using the $k$-nearest neighbor method.
Keyword (in Japanese) (See Japanese page) 
(in English) $k$-nearest neighbor / data-driven science / Selective Inference / Over-Conditioning / Parametric SI / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 86, IBISML2024-8, pp. 51-59, June 2024.
Paper # IBISML2024-8 
Date of Issue 2024-06-13 (NC, IBISML) 
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)
Download PDF NC2024-8 IBISML2024-8

Conference Information
Committee IBISML NC IPSJ-BIO IPSJ-MPS  
Conference Date 2024-06-20 - 2024-06-22 
Place (in Japanese) (See Japanese page) 
Place (in English) OIST 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2024-06-IBISML-NC-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Selective Inference for Reliability Quantification of k-Nearest Neighbor Anomoaly Detection 
Sub Title (in English)  
Keyword(1) $k$-nearest neighbor  
Keyword(2) data-driven science  
Keyword(3) Selective Inference  
Keyword(4) Over-Conditioning  
Keyword(5) Parametric SI  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Mizuki Niihori  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Masaya Ikuta  
2nd Author's Affiliation Nagoya Institute of Technology (NITech)
3rd Author's Name Akihumi Yamada  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Kouichi Taji  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
5th Author's Name Ichiro Takeuchi  
5th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2024-06-20 15:00:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # NC2024-8, IBISML2024-8 
Volume (vol) vol.124 
Number (no) no.85(NC), no.86(IBISML) 
Page pp.51-59 
#Pages 9 
Date of Issue 2024-06-13 (NC, IBISML) 

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