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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
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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)  
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
Date of Issue 2013-11-05 (IBISML) 


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