A New Hybrid Sampling Approach for Classification of Imbalanced Datasets

Abstract

Nowadays it is an era of data driven. Many organizations around the world including bank, industry, commercial, and medical intend to extract knowledge from a huge of data. But in the real-word datasets, most of them occur class imbalance problems. This paper presents a new algorithm to handle an imbalanced classification. The proposed technique is a hybrid sampling approach which is the combination of a well know oversampling algorithm called SMOTE and the undersampling technique by removing the ambiguous instances from the majority class instances. The experimental results show that the new hybrid sampling method yields the better predictive performance in term of F-measure when compare with other sampling techniques. In addition, it can improve f-measure up to 59.73% and 412.26% when compare with the original dataset based on decision tree learning and naïve bayes classifiers respectively.