Paper Abstract and Keywords |
Presentation |
2013-11-12 15:45
[Poster Presentation]
A boosting method considering tolerance against noisy data by weighting each data according to the distance between incidents Shinjiro Fujita, Sayaka Kamei, Satoshi Fujita (Hiroshima Univ.) IBISML2013-38 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
AdaBoost is one of the major ensemble learning methods. It is easy to implement and
has high classification accuracy. However, AdaBoost has a problem that the accuracy
gets worse when there are noisy incidents in training data because it is likely to overfit
against them. Therefore, in this paper, we propose a boosting method which has tolerance for
noisy incidents. Specifically, the method detects noisy incidents in training data by considering distribution
of training data in the concept space, and reduces the effect of noisy incidents by giving them small weights.
Finally, we conduct an experiment on selected datasets that the proposed method is more
robust than standard and other types of AdaBoost for noisy datasets. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
machine learning / ensemble learning / boosting / AdaBoost / noise / / / |
Reference Info. |
IEICE Tech. Rep., vol. 113, no. 286, IBISML2013-38, pp. 15-21, Nov. 2013. |
Paper # |
IBISML2013-38 |
Date of Issue |
2013-11-05 (IBISML) |
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) |
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IBISML2013-38 |
Conference Information |
Committee |
IBISML |
Conference Date |
2013-11-10 - 2013-11-13 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tokyo Institute of Technology, Kuramae-Kaikan |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
The 16th IBIS Workshop & The 2nd IBIS Tutorial |
Paper Information |
Registration To |
IBISML |
Conference Code |
2013-11-IBISML |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A boosting method considering tolerance against noisy data by weighting each data according to the distance between incidents |
Sub Title (in English) |
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Keyword(1) |
machine learning |
Keyword(2) |
ensemble learning |
Keyword(3) |
boosting |
Keyword(4) |
AdaBoost |
Keyword(5) |
noise |
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1st Author's Name |
Shinjiro Fujita |
1st Author's Affiliation |
Hiroshima University (Hiroshima Univ.) |
2nd Author's Name |
Sayaka Kamei |
2nd Author's Affiliation |
Hiroshima University (Hiroshima Univ.) |
3rd Author's Name |
Satoshi Fujita |
3rd Author's Affiliation |
Hiroshima University (Hiroshima Univ.) |
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Speaker |
Author-1 |
Date Time |
2013-11-12 15:45:00 |
Presentation Time |
180 minutes |
Registration for |
IBISML |
Paper # |
IBISML2013-38 |
Volume (vol) |
vol.113 |
Number (no) |
no.286 |
Page |
pp.15-21 |
#Pages |
7 |
Date of Issue |
2013-11-05 (IBISML) |
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