Over-sampling via under-sampling in strongly imbalanced data

Abstract

Classification of imbalanced data sets is an important challenge in machine learning. Whenever the size of one of the classes is very smaller than others, it is called as imbalanced data sets. In these types of data sets, all the algorithms and efficiency criteria care the majority class and ignore the minority class. But sometimes these minority class contains important information and ignoring that information may be cause wrong total results, especially whenever, the accuracy of results are very important such as medical data. Therefore, proposing approaches for solving this problem is necessary. One of the best approaches to solve this problem is re-sampling. Re-sampling runs usually as an extra preprocessing step and it has two main methods named as over-sampling and under-sampling. This investigation analysis the effect of Ratio imbalance and the selected classifier on the application of several re-sampling strategies to deal with imbalanced data sets. We applied two different classifiers (J48 and Naïve Bays), four resampling algorithms (Org, SMOTE, Borderline SMOTE, OSS and NCL approaches) and four Performance assessment measures (TPrate , TNrate, Gmean and AUC) on 13 sets of real data. Our experimental results show that, whenever, data sets are strongly imbalanced, over- sampling methods are more efficient in compare with under-sampling methods. Moreover, our results indicates that, when dealing with imbalanced data with any level, applying resampling techniques is preferred. Further, the results indicate that the classifier has very poor influence on the effectiveness of the resampling strategies.