Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

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
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 IBISML2022-36

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  
Keyword(6)  
Keyword(7)  
Keyword(8)  
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)
3rd Author's Name  
3rd Author's Affiliation ()
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
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 
#Pages 6 
Date of Issue 2022-09-08 (IBISML) 

[Return to Top Page]

[Return to IEICE Web Page]

(Line Height for English Pages: 1)
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan