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Paper Abstract and Keywords
Presentation 2016-07-06 10:25
A Semi-supervised Learning Method for Imbalanced Binary Classification
Akinori Fujino, Naonori Ueda (NTT) IBISML2016-3
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a semi-supervised learning method for imbalanced binary classification where the number of positive samples differs largely from that of negative samples. The area under the ROC curve (AUC) is often used as an effective measure for evaluating binary classifiers in such imbalanced tasks, and thus AUC-optimized classifiers have been developed which were trained to maximize an AUC value measured on a labeled sample set. The proposed method utilizes generative models for assisting the incorporation of unlabeled samples in AUC-optimized classifiers. We applied the proposed method to text classification by employing a naive Bayes model as the generative model. Using two benchmark datasets, we confirmed experimentally that the proposed method was more useful for imbalanced binary classification than conventional semi-supervised learning methods based on discriminative, generative, and those hybrid models. We also confirmed the effect of using generative models for semi-supervised learning of AUC-optimized classifiers.
Keyword (in Japanese) (See Japanese page) 
(in English) Semi-supervised Learning / AUC Maximization / Generative Model / Naive Bayes Model / Text Classification / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 121, IBISML2016-3, pp. 195-200, July 2016.
Paper # IBISML2016-3 
Date of Issue 2016-06-28 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee NC IPSJ-BIO IBISML IPSJ-MPS  
Conference Date 2016-07-04 - 2016-07-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2016-07-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Semi-supervised Learning Method for Imbalanced Binary Classification 
Sub Title (in English)  
Keyword(1) Semi-supervised Learning  
Keyword(2) AUC Maximization  
Keyword(3) Generative Model  
Keyword(4) Naive Bayes Model  
Keyword(5) Text Classification  
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1st Author's Name Akinori Fujino  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Naonori Ueda  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2016-07-06 10:25:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2016-3 
Volume (vol) vol.116 
Number (no) no.121 
Page pp.195-200 
#Pages
Date of Issue 2016-06-28 (IBISML) 


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