Classifying Multiple Imbalanced Attributes in Relational Data

Authors:
Amal S. Ghanem;Svetha Venkatesh;Geoff West
Affiliations:
Curtin University of Technology, Perth, Western Australia 6845;Curtin University of Technology, Perth, Western Australia 6845;Curtin University of Technology, Perth, Western Australia 6845
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

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Abstract

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 non-relational data and mainly for classifying one single attribute. In this paper, we propose an approach for learning from relational data with the 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.