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 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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IBISML2016-3 |
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) |
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Semi-supervised Learning |
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AUC Maximization |
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Generative Model |
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Naive Bayes Model |
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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 |
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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 |
6 |
Date of Issue |
2016-06-28 (IBISML) |
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