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Paper Abstract and Keywords
Presentation 2022-09-15 14:00
Interpretable Model Combining statements and DNN
Ryo Okuda, Yuya Yoshikawa (STAIR) IBISML2022-36
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we propose a method that achieves both interpretability of Decision Tree and the prediction accuracy of Deep Neural Networks (DNN). Random Forest is a method that divides the feature space by the boundaries indicated by multiple statements and has good interpretability. However, interpretation becomes difficult, if the number of boundaries is too large. There are existing methods that use an approximated model to reduce the number of boundaries while preserving the prediction accuracy.
Although these methods can reduce the number of boundaries, the prediction accuracy of the newly obtained model tends to be lower than that of the model before approximation. Therefore, in this study, we propose a DNN that selects a small number of statements for each sample and makes predictions using the weights assigned to these statements. In experiments, we show that the proposed method improves the accuracy more than Random Forest alone.
Keyword (in Japanese) (See Japanese page) 
(in English) Interpretability / Deep Neural Networks / Decision Tree / Linear Model / Random Forest / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 189, IBISML2022-36, pp. 25-30, Sept. 2022.
Paper # IBISML2022-36 
Date of Issue 2022-09-08 (IBISML) 
ISSN Online edition: ISSN 2432-6380
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)
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Conference Information
Committee IBISML  
Conference Date 2022-09-15 - 2022-09-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Keio Univ. (Yagami Campus) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2022-09-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Interpretable Model Combining statements and DNN 
Sub Title (in English)  
Keyword(1) Interpretability  
Keyword(2) Deep Neural Networks  
Keyword(3) Decision Tree  
Keyword(4) Linear Model  
Keyword(5) Random Forest  
1st Author's Name Ryo Okuda  
1st Author's Affiliation STAIR Lab (STAIR)
2nd Author's Name Yuya Yoshikawa  
2nd Author's Affiliation STAIR Lab (STAIR)
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Speaker Author-1 
Date Time 2022-09-15 14:00:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2022-36 
Volume (vol) vol.122 
Number (no) no.189 
Page pp.25-30 
Date of Issue 2022-09-08 (IBISML) 

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