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Paper Abstract and Keywords
Presentation 2024-12-26 13:00
Enhancement of Outlier Detection Using XGBOD through Quantum Kernel-Based Unsupervised Representation Learning
Ryotaro Nagumo (Hachinohe Inst. of Tech.), Hirokazu Shimauchi (Future Univ. Hakodate), Shun Kumagai (Hachinohe Inst. of Tech.) DE2024-13
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we aim to improve the performance of XGBOD by incorporating quantum kernels. XGBOD is a method that enhances detection performance by selecting useful features from the outputs of multiple unsupervised outlier detection techniques, integrating these features with the original features. By introducing unsupervised outlier detection based on quantum kernels, the proposed approach allows for the generation of complex feature representations that cannot be achieved with conventional XGBOD, potentially leading to improved performance. Experiments conducted on six datasets confirmed that, under certain conditions, the performance of outlier detection is improved.
Keyword (in Japanese) (See Japanese page) 
(in English) Quantum Kernel / Extreme Gradient Boosting Outlier Detection / Unsupervised Representation Learning / One-Class SVM / Outlier Detection / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 323, DE2024-13, pp. 7-12, Dec. 2024.
Paper # DE2024-13 
Date of Issue 2024-12-19 (DE) 
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 DE2024-13

Conference Information
Committee DE IPSJ-DBS IPSJ-IFAT  
Conference Date 2024-12-26 - 2024-12-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To DE 
Conference Code 2024-12-DE-DBS-IFAT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhancement of Outlier Detection Using XGBOD through Quantum Kernel-Based Unsupervised Representation Learning 
Sub Title (in English)  
Keyword(1) Quantum Kernel  
Keyword(2) Extreme Gradient Boosting Outlier Detection  
Keyword(3) Unsupervised Representation Learning  
Keyword(4) One-Class SVM  
Keyword(5) Outlier Detection  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Ryotaro Nagumo  
1st Author's Affiliation Hachinohe Institute of Technology (Hachinohe Inst. of Tech.)
2nd Author's Name Hirokazu Shimauchi  
2nd Author's Affiliation Future University Hakodate (Future Univ. Hakodate)
3rd Author's Name Shun Kumagai  
3rd Author's Affiliation Hachinohe Institute of Technology (Hachinohe Inst. of Tech.)
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Speaker Author-1 
Date Time 2024-12-26 13:00:00 
Presentation Time 25 minutes 
Registration for DE 
Paper # DE2024-13 
Volume (vol) vol.124 
Number (no) no.323 
Page pp.7-12 
#Pages 6 
Date of Issue 2024-12-19 (DE) 

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