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Paper Abstract and Keywords
Presentation 2025-01-13 10:50
Application of the Statistical Test Rule Induction Method to Classification Problems and Comparisons with the Neural Network Method
Ji Kaikuan, Tomoshi Hatakeyama, Tetsuro Saeki (Yamaguchi Univ.), Yuichi Kato (Shimane Univ.) MSS2024-57 SS2024-36
Abstract (in Japanese) (See Japanese page) 
(in English) After pointing out the problems of the conventional Rough Sets’ methods that induce if-then rules hidden in a dataset called the decision table (DT), we proposed a new model for generating the DT and developed a method, statistical test rule induction method (STRIM), for inducing rules in the DT from a statistical perspective. The validity and effectiveness of STRIM were confirmed by applying it to the DT generated based on the data generation model (DGM). In general, the effectiveness of the methods inducing rules is often confirmed by applying them to classification problems that predict outputs against new inputs. From this perspective, we applied an expanded STRIM (ex-STRIM) to a classification problem and confirmed its effectiveness. This paper applies a Feedforward Neural Network and a modified ex-STRIM to the classification problem under the DGM and compares their classification performance and features as a classification method. Although the classification results strongly depend on the latent rules in the dataset and the number of learning datasets, the similarities and differences between the two methods are discussed; for example, both methods use different approaches to realize a conditional probability distribution of the output, given the input.
Keyword (in Japanese) (See Japanese page) 
(in English) decision table / if-then rule / classification problem / statistical test / feedforward neural network / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 325, MSS2024-57, pp. 76-81, Jan. 2025.
Paper # MSS2024-57 
Date of Issue 2025-01-05 (MSS, SS) 
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 MSS2024-57 SS2024-36

Conference Information
Committee MSS SS  
Conference Date 2025-01-12 - 2025-01-13 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MSS 
Conference Code 2025-01-MSS-SS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Application of the Statistical Test Rule Induction Method to Classification Problems and Comparisons with the Neural Network Method 
Sub Title (in English)  
Keyword(1) decision table  
Keyword(2) if-then rule  
Keyword(3) classification problem  
Keyword(4) statistical test  
Keyword(5) feedforward neural network  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Ji Kaikuan  
1st Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
2nd Author's Name Tomoshi Hatakeyama  
2nd Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
3rd Author's Name Tetsuro Saeki  
3rd Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
4th Author's Name Yuichi Kato  
4th Author's Affiliation Shimane University (Shimane Univ.)
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Speaker Author-1 
Date Time 2025-01-13 10:50:00 
Presentation Time 25 minutes 
Registration for MSS 
Paper # MSS2024-57, SS2024-36 
Volume (vol) vol.124 
Number (no) no.325(MSS), no.326(SS) 
Page pp.76-81 
#Pages 6 
Date of Issue 2025-01-05 (MSS, SS) 

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