A Comprehensive Review on the Issue of Class Imbalance in Predictive Modelling

One of the factors that affect the performance of the classification predictive modelling task is the skewed distribution of the classes in the training data, which is known as the “class imbalance problem”. It hinders the performance of the predictive models. The contribution of this paper is threefold; primarily, this paper discusses the latest techniques used in different domains to handle class imbalance. Then, the taxonomy of data preprocessing, algorithm level and hybrid techniques have been presented. Finally, various evaluation strategies and application domains are presented. This paper covers all the latest and most effective techniques and elaborates on effective techniques such as cost-sensitive learning and ensemble learning, which collectively provide the reader with an extensive overview of the techniques used by researchers to alleviate skewed class distribution of the classes in the training data.