venkatesh-multiclasspattern-2010.pdf (299.46 kB)
Multi-class pattern classification in imbalanced data
conference contribution
posted on 2010-01-01, 00:00 authored by A Ghanem, Svetha VenkateshSvetha Venkatesh, G WestThe 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.
History
Event
International Conference on Pattern Recognition (20th : 2010 : Istanbul, Turkey)Pagination
2881 - 2884Publisher
IEEELocation
Istanbul, TurkeyPlace of publication
Los Alamitos, Calif.Publisher DOI
Start date
2010-08-23End date
2010-08-26ISSN
1051-4651ISBN-13
9781424475421ISBN-10
1424475422Language
engNotes
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E1.1 Full written paper - refereedCopyright notice
2010, IEEEEditor/Contributor(s)
J GuerreroTitle of proceedings
ICPR 2010 : Proceedings : 20th International Conference on Pattern RecognitionUsage metrics
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