Imbalanced data classification: A novel re-sampling approach combining versatile improved SMOTE and rough sets

Abstract

In recent years, the problem of learning from imbalanced data has emerged as important and challenging. The fact that one of the classes is underrepresented in the data set is not the only reason of difficulties. The complex distribution of data, especially small disjuncts, noise and class overlapping, contributes to the significant depletion of classifier’s performance. Hence, the numerous solutions were proposed. They are categorized into three groups: data-level techniques, algorithm-level methods and cost-sensitive approaches. This paper presents a novel datalevel method combining Versatile Improved SMOTE and rough sets. The algorithm was applied to the two-class problems, data sets were characterized by the nominal attributes. We evaluated the proposed technique in comparison with other preprocessing methods. The impact of the additional cleaning phase was specifically verified.