Classifying Multiple imbalanced attributes in relational data - Ghanem, Amal; Venkatesh, Svetha; West, Geoffrey (Springer Berlin / Heidelberg, 2009)
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.