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| 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.)  IT2019-17 |
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| 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 | |
| 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 | IT2019-17 |
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| 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 |
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| 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.) |
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| Speaker | Author-1 |
| Date Time | 2019-07-25 14:50:00 |
| Presentation Time | 25 minutes |
| Registration for | IT |
| Paper # | IT2019-17 |
| Volume (vol) | vol.119 |
| Number (no) | no.149 |
| Page | pp.11-16 |
| #Pages | 6 |
| Date of Issue | 2019-07-18 (IT) |
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