A Novel Approach for Handling Outliers in Imbalanced Data

Most of the traditional classification algorithms assume their training data to be well-balanced in terms of class distribution. Real-world datasets, however, are imbalanced in nature thus degrade the performance of the traditional classifiers. To solve this problem, many strategies are adopted to balance the class distribution at the data level. The data level methods balance the imbalance distribution between majority and minority classes using either oversampling or under sampling techniques. The main concern of this paper is to remove the outliers that may generate while using oversampling techniques. In this study, we proposed a novel approach for solving the class imbalance problem at data level by using modified SMOTE to remove the outliers that may exist after synthetic data generation using SMOTE oversampling technique. We extensively compare our approach with SMOTE, SMOTE+ENN, SMOTE+Tomek-Link using 9 datasets from keel repository using classification algorithms. The result reveals that our approach improves the prediction performance for most of the classification algorithms and achieves better performance compared to the existing approaches.