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. Attached Files Name Description MIMEType Size Downloads venkatesh-multiclasspattern-2010.pdf Published version application/pdf 299.46KB 164 Title Multi-class pattern classification in imbalanced data Author(s) Ghanem, Amal S. 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 Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 20 Apr 2012, 11:29:46 EST Tue, 24 Apr 2012, 14:24:13 EST Tue, 24 Apr 2012, 14:26:24 EST Tue, 29 Jul 2014, 14:32:23 EST Mon, 15 Sep 2014, 14:19:44 EST Tue, 06 Sep 2016, 18:40:54 EST Tue, 05 Dec 2017, 12:31:09 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 21 times in Scopus Search Google Scholar Access Statistics: 351 Abstract Views, 162 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:29:46 EST

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