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Multi-class pattern classification in imbalanced data

Ghanem, Amal S., Venkatesh, Svetha and West, Geoff 2010, Multi-class pattern classification in imbalanced data, in ICPR 2010 : Proceedings : 20th International Conference on Pattern Recognition, IEEE, Los Alamitos, Calif., pp. 2881-2884, doi: 10.1109/ICPR.2010.706.

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Title Multi-class pattern classification in imbalanced data
Author(s) Ghanem, Amal S.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
West, Geoff
Conference name International Conference on Pattern Recognition (20th : 2010 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 23-26 Aug. 2010
Title of proceedings ICPR 2010 : Proceedings : 20th International Conference on Pattern Recognition
Editor(s) Guerrero, Juan E.
Publication date 2010
Conference series International Conference on Pattern Recognition
Start page 2881
End page 2884
Total pages 4
Publisher IEEE
Place of publication Los Alamitos, Calif.
Keyword(s) ensemble learning
imbalanced class problem
multi-class classification
Summary The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper we present our research in learning from imbalanced multi-class data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the two-class problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational and non-relational domains.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 1424475422
9781424475421
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2010.706
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2010, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044527

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.