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| Presentation | 2022-07-22 15:05 Bayes Optimal Approximation Algorithm by Boosting-like Construction of Meta-Tree Sets in Classification on Decision Tree Model Ryota Maniwa, Naoki Ichijo, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.)  IT2022-28 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Decision trees are used for classification and regression such as predicting the objective variable corresponding to the new explanatory variable. In this study, decision trees are regarded as a data generation model and we propose a prediction method based on Bayes decision theory. However, the computational complexity of calculating the prediction increases exponentially with the number of tree models, the depth of trees, and the dimensions of explanatory variables. There is a concept called ``meta-tree’’ which reduces the computational complexity in terms of the tree depth. Therefore, we also propose an efficient algorithm to predict the objective variable by creating multiple meta-trees sequentially. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Bayes Optimal Prediction / Decision Tree / Ensemble learning / Boosting / Meta-tree / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 128, IT2022-28, pp. 67-72, July 2022. | |
| Paper # | IT2022-28 | |
| Date of Issue | 2022-07-14 (IT) | |
| 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 | IT2022-28 |
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| Conference Information | |
| Committee | IT |
| Conference Date | 2022-07-21 - 2022-07-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okayama University of Science |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Freshman session, General |
| Paper Information | |
| Registration To | IT |
| Conference Code | 2022-07-IT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Bayes Optimal Approximation Algorithm by Boosting-like Construction of Meta-Tree Sets in Classification on Decision Tree Model |
| Sub Title (in English) | |
| Keyword(1) | Bayes Optimal Prediction |
| Keyword(2) | Decision Tree |
| Keyword(3) | Ensemble learning |
| Keyword(4) | Boosting |
| Keyword(5) | Meta-tree |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Ryota Maniwa |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Naoki Ichijo |
| 2nd Author's Affiliation | Waseda University (Waseda Univ.) |
| 3rd Author's Name | Koshi Shimada |
| 3rd Author's Affiliation | Waseda University (Waseda Univ.) |
| 4th Author's Name | Toshiyasu Matsushima |
| 4th Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-07-22 15:05:00 |
| Presentation Time | 25 minutes |
| Registration for | IT |
| Paper # | IT2022-28 |
| Volume (vol) | vol.122 |
| Number (no) | no.128 |
| Page | pp.67-72 |
| #Pages | 6 |
| Date of Issue | 2022-07-14 (IT) |
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