A Review on Imbalanced Data Classification Techniques

Abstract

Most all datasets that hold real-time data have an imbalanced organization of class instances. The total quantity of instances in certain classes is substantially greater than other classes and this skewed nature in the arrangement of classes is called Class Imbalance Problem (CIP). This imbalanced data affects the prediction performance since this forecast the weak class data samples wrongly. CIP is experienced by data mining professionals in a broad range of sectors. The categorization of imbalanced data is a huge challenge that arises in the discipline of Machine Learning (ML) and Deep Learning (DL). It is the critical issue that emerged for research and the deployment of sampling strategies to enhance the performance of the classifier has attracted extensive interest in the literature review. In this study, the importance of organizing imbalanced data is explained and the techniques suggested by the different scholars to counterbalance the skewed nature of classes and the assessment criteria for measuring the accuracy and prediction rate of the different classifiers have indeed been examined.