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Paper Abstract and Keywords
Presentation 2005-11-18 15:00
A Selection Criterion for Robust Classifiers by Considering the Variance of Test Performance
Ikumi Suzuki, Shigeyuki Oba, Junichiro Hirayama, Shin Ishii (NAIST)
Abstract (in Japanese) (See Japanese page) 
(in English) In classification problems with gene expression profiles emerged from clinical field, we usually compare performances of multiple classifiers and select the best classifier by standard cross-validation technique. Reliability of the selected classifier is, however, often low due to the large expected variance of test performance caused by the small sample size compared to the high-dimensionality of gene expression data. In this study, we propose a new method, the Parametric Noise Bootstrap and Percentile (PNBP) method which selects a classifier that tends to select poor classifier avoiding a high risk of resulting in a poor performance. The PNBP method evaluates the risk of selecting poor clasifier by applying cross-validation to artificial datasets those are created by a bootstrap method. We applied the PNBP method to determine the number of input gene in Weighted-Voting (WV) classification method and examined the basic property of the PNBP for real gene expression data. The result showed that the PNBP method select a large number of input gene when the sample size is small, in contrast that the standard method often select an extremely and unrealistically small number. This suggests that our PNBP criterion is robust for the variance of test performance evaluated by cross-validation and works as a risk-avoiding for model selection criterion.
Keyword (in Japanese) (See Japanese page) 
(in English) Microarray / Classifier Performance Estimation / Bootstrap / Model selection / / / /  
Reference Info. IEICE Tech. Rep., vol. 105, no. 416, NLP2005-66, pp. 25-30, Nov. 2005.
Paper # NLP2005-66 
Date of Issue 2005-11-11 (NLP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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Conference Information
Committee NLP  
Conference Date 2005-11-18 - 2005-11-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyushu Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Special session and general. Subject of special session is ``Randomness and prediction---from fundamentals to applications''. 
Paper Information
Registration To NLP 
Conference Code 2005-11-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Selection Criterion for Robust Classifiers by Considering the Variance of Test Performance 
Sub Title (in English)  
Keyword(1) Microarray  
Keyword(2) Classifier Performance Estimation  
Keyword(3) Bootstrap  
Keyword(4) Model selection  
1st Author's Name Ikumi Suzuki  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Shigeyuki Oba  
2nd Author's Affiliation Nara Institute of Science and Technology (NAIST)
3rd Author's Name Junichiro Hirayama  
3rd Author's Affiliation Nara Institute of Science and Technology (NAIST)
4th Author's Name Shin Ishii  
4th Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Date Time 2005-11-18 15:00:00 
Presentation Time 25 
Registration for NLP 
Paper # IEICE-NLP2005-66 
Volume (vol) IEICE-105 
Number (no) no.416 
Page pp.25-30 
#Pages IEICE-6 
Date of Issue IEICE-NLP-2005-11-11 

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