IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
... (for ESS/CS/ES/ISS)
Tech. Rep. Archives
... (for ES/CS)
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2019-07-25 14:50
Bayes Optimal Classification on Decision Tree Model and Its Approximative Algorithm Using Ensemble Learning
Nao Dobashi, Shota Saito, Toshiyasu Matsushima (Waseda Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper we consider classification problem about discrete category $y$ regarding discrete variables $bm{x}$. Decision tree model is one of the model expressing $P(y|bm{x})$. Previously, regarding the classification problem whose $P(y|bm{x})$ is unknown, algorithms which estimate one or more decision tree models from given data and classify using them have been studied. However, these algorithms are not necessarily theoretically optimal. On the other hand, Suko et al. proposed Bayes optimal classification by considering every decision tree model (model class). However, we have to calculate the sum of every decision tree model which is included in model class in order to do this. Therefore, they proposed an algorithm achiving Bayes optimal classification under restriction. In addition, Arai et al. proposed an approximative algorithm under moderate restriction. In our study, we propose an approximative algorithm of the Bayes optimal classification. In this algorithm, we construct multiple model classes based on the idea of ensemble learning and average the outputs of these model classes with their posterior distributions. Also, experiments using synthetic data are performed to make sure its effectiveness and performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Decision Tree Model / Ensemble Learning / Bayes Optimal Classification / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 149, IT2019-17, pp. 11-16, July 2019.
Paper # IT2019-17 
Date of Issue 2019-07-18 (IT) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380

Conference Information
Committee IT  
Conference Date 2019-07-25 - 2019-07-26 
Place (in Japanese) (See Japanese page) 
Place (in English) NATULUCK-Iidabashi-Higashiguchi Ekimaeten 
Topics (in Japanese) (See Japanese page) 
Topics (in English) freshman session, general 
Paper Information
Registration To IT 
Conference Code 2019-07-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Bayes Optimal Classification on Decision Tree Model and Its Approximative Algorithm Using Ensemble Learning 
Sub Title (in English)  
Keyword(1) Decision Tree Model  
Keyword(2) Ensemble Learning  
Keyword(3) Bayes Optimal Classification  
1st Author's Name Nao Dobashi  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Shota Saito  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Toshiyasu Matsushima  
3rd Author's Affiliation Waseda University (Waseda Univ.)
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 ()
Date Time 2019-07-25 14:50:00 
Presentation Time 25 
Registration for IT 
Paper # IEICE-IT2019-17 
Volume (vol) IEICE-119 
Number (no) no.149 
Page pp.11-16 
#Pages IEICE-6 
Date of Issue IEICE-IT-2019-07-18 

[Return to Top Page]

[Return to IEICE Web Page]

The Institute of Electronics, Information and Communication Engineers (IEICE), Japan