Classifying Multiple imbalanced attributes in relational data
Real-world data are often stored as relational database systems with different numbers of significant attributes. Unfortunately, most classification techniques are proposed for learning from balanced nonrelational data and mainly for classifying one single attribute. In this paper, we propose an app...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Springer Berlin / Heidelberg
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/6513 |
| _version_ | 1848745096727494656 |
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| author | Ghanem, Amal Venkatesh, Svetha West, Geoffrey |
| author2 | Ann Nicholson |
| author_facet | Ann Nicholson Ghanem, Amal Venkatesh, Svetha West, Geoffrey |
| author_sort | Ghanem, Amal |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Real-world data are often stored as relational database systems with different numbers of significant attributes. Unfortunately, most classification techniques are proposed for learning from balanced nonrelational data and mainly for classifying one single attribute. In this paper, we propose an approach for learning from relational data withthe specific goal of classifying multiple imbalanced attributes. In our approach, we extend a relational modelling technique (PRMs-IM) designed for imbalanced relational learning to deal with multiple imbalanced attributes classification. We address the problem of classifying multiple imbalanced attributes by enriching the PRMs-IM with the 'Bagging' classification ensemble. We evaluate our approach on real-world imbalanced student relational data and demonstrate its effectiveness in predicting student performance. |
| first_indexed | 2025-11-14T06:11:56Z |
| format | Conference Paper |
| id | curtin-20.500.11937-6513 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:11:56Z |
| publishDate | 2009 |
| publisher | Springer Berlin / Heidelberg |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-65132022-12-09T05:23:40Z Classifying Multiple imbalanced attributes in relational data Ghanem, Amal Venkatesh, Svetha West, Geoffrey Ann Nicholson Xiaodong Li Real-world data are often stored as relational database systems with different numbers of significant attributes. Unfortunately, most classification techniques are proposed for learning from balanced nonrelational data and mainly for classifying one single attribute. In this paper, we propose an approach for learning from relational data withthe specific goal of classifying multiple imbalanced attributes. In our approach, we extend a relational modelling technique (PRMs-IM) designed for imbalanced relational learning to deal with multiple imbalanced attributes classification. We address the problem of classifying multiple imbalanced attributes by enriching the PRMs-IM with the 'Bagging' classification ensemble. We evaluate our approach on real-world imbalanced student relational data and demonstrate its effectiveness in predicting student performance. 2009 Conference Paper http://hdl.handle.net/20.500.11937/6513 10.1007/978-3-642-10439-8_23 Springer Berlin / Heidelberg fulltext |
| spellingShingle | Ghanem, Amal Venkatesh, Svetha West, Geoffrey Classifying Multiple imbalanced attributes in relational data |
| title | Classifying Multiple imbalanced attributes in relational data |
| title_full | Classifying Multiple imbalanced attributes in relational data |
| title_fullStr | Classifying Multiple imbalanced attributes in relational data |
| title_full_unstemmed | Classifying Multiple imbalanced attributes in relational data |
| title_short | Classifying Multiple imbalanced attributes in relational data |
| title_sort | classifying multiple imbalanced attributes in relational data |
| url | http://hdl.handle.net/20.500.11937/6513 |