ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
This addresses classification problems with imbalanced datasets, particularly for multi-class scenarios, though it is incremental as it extends existing methods to multi-class settings.
The paper tackled multi-class imbalanced learning by empirically analyzing dynamic ensemble selection and data preprocessing methods, finding that dynamic ensembles improved AUC and G-mean compared to static ensembles on 26 datasets.
Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been reported to yield promising results. However, the majority of ensemble methods applied to imbalanced learning are static ones. Moreover, they only deal with binary imbalanced problems. Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems. We considered five variations of preprocessing methods and fourteen dynamic selection schemes. Our experiments conducted on 26 multi-class imbalanced problems show that the dynamic ensemble improves the AUC and the G-mean as compared to the static ensemble. Moreover, data preprocessing plays an important role in such cases.