Abstract
Imbalanced data is a problem which is observed in many real-world applications. Although a lot of research is focused on achieving a solution to handle this problem, most of them assume binary classes. However, occurrence of multiple classes in most of the applications is not uncommon. Multiclass classification with imbalanced data poses additional challenges. This paper proposes a hybrid ensemble approach for classification of multiclass imbalanced data (HECMI). A hybrid of data based and algorithm based approach is proposed to deal with the imbalance and multiple classes. The ensemble created focuses on misclassified instances that are added to the partitioned dataset. HECMI proves to be more accurate than traditional algorithms.
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