venkatesh-learningin-2008.pdf (161.69 kB)
Learning in imbalanced relational data
conference contribution
posted on 2008-01-01, 00:00 authored by A Ghanem, Svetha VenkateshSvetha Venkatesh, G WestTraditional 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.
History
Event
International Conference on Pattern Recognition (19th : 2008 : Tampa, Fla.)Pagination
1 - 4Publisher
IEEELocation
Tampa, Fla.Place of publication
Washington, D. C.Publisher DOI
Start date
2008-12-08End date
2008-12-11ISSN
1051-4651ISBN-13
9781424421749ISBN-10
1424421748Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2008, IEEETitle of proceedings
ICPR 2008 : Proceedings of the 19th International Conference on Pattern RecognitionUsage metrics
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