An experimental comparison of MES aggregation rules in case of imbalanced datasets
Paolo Soda · 2009
Learning under imbalanced dataset can be difficult since traditional algorithms are biased towards the majority class, providing low predictive accuracy over the minority one. Among the several methods proposed in the literature to overcome such a limitation, the most recent uses multi-experts system (MES) composed of balanced classifiers, whose decisions are aggregated according to a combination rule. Each classifier of the MES is trained with a balanced subset of the original training set, which can be determined applying different division methods. This paper explores how different MES combination rules perform with imbalanced TS, experimentally comparing fusion and selection combination criteria. Furthermore, two methods have been used to divide the original TS, namely random selection and clustering. The results confirm and extend previous findings reported in the literature showing that, on the one side, combination rules belonging to selection framework outperform the others and, on the other side, dividing the original training set via random selection rather than clustering permits to attain better performance.