You are not logged in.
Openly accessible

Learning in imbalanced relational data

Ghanem, Amal S., Venkatesh, Svetha and West, Geoff 2008, Learning in imbalanced relational data, in ICPR 2008 : Proceedings of the 19th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 1-4, doi: 10.1109/ICPR.2008.4761095.

Attached Files
Name Description MIMEType Size Downloads
venkatesh-learningin-2008.pdf Published version application/pdf 161.69KB 83

Title Learning in imbalanced relational 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 (19th : 2008 : Tampa, Fla.)
Conference location Tampa, Fla.
Conference dates 8-11 Dec. 2008
Title of proceedings ICPR 2008 : Proceedings of the 19th International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2008
Conference series International Conference on Pattern Recognition
Start page 1
End page 4
Total pages 4
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) data files
imbalanced class
imbalanced data
new model
probabilistic relational models
real world data
relational data
relational Database
relational learning
relational systems
similar numbers
traditional learning
Summary Traditional learning techniques learn from flat data files with the assumption that each class has a similar number of examples. However, the majority of real-world data are stored as relational systems with imbalanced data distribution, where one class of data is over-represented as compared with other classes. We propose to extend a relational learning technique called Probabilistic Relational Models (PRMs) to deal with the imbalanced class problem. We address learning from imbalanced relational data using an ensemble of PRMs and propose a new model: the PRMs-IM. We show the performance of PRMs-IM on a real university relational database to identify students at risk.
ISBN 1424421748
9781424421749
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2008.4761095
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 ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044586

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
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
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 4 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 237 Abstract Views, 83 File Downloads  -  Detailed Statistics
Created: Fri, 20 Apr 2012, 11:33:51 EST

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.