Learning in imbalanced relational data
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 o...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IAPR
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/2826 |
| _version_ | 1848744059698413568 |
|---|---|
| author | Ghanem, Amal Venkatesh, Svetha West, Geoff |
| author2 | M. Ejiri |
| author_facet | M. Ejiri Ghanem, Amal Venkatesh, Svetha West, Geoff |
| author_sort | Ghanem, Amal |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T05:55:27Z |
| format | Conference Paper |
| id | curtin-20.500.11937-2826 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T05:55:27Z |
| publishDate | 2008 |
| publisher | IAPR |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-28262022-11-21T05:19:38Z Learning in imbalanced relational data Ghanem, Amal Venkatesh, Svetha West, Geoff M. Ejiri R. Kasturi G. Sanniti di Baja 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. 2008 Conference Paper http://hdl.handle.net/20.500.11937/2826 10.1109/ICPR.2008.4761095 IAPR restricted |
| spellingShingle | Ghanem, Amal Venkatesh, Svetha West, Geoff Learning in imbalanced relational data |
| title | Learning in imbalanced relational data |
| title_full | Learning in imbalanced relational data |
| title_fullStr | Learning in imbalanced relational data |
| title_full_unstemmed | Learning in imbalanced relational data |
| title_short | Learning in imbalanced relational data |
| title_sort | learning in imbalanced relational data |
| url | http://hdl.handle.net/20.500.11937/2826 |