Information granulation construction and representation strategies for classification in imbalanced data based on granular computing

Imbalanced datasets play an important role in many fields in real applications such as medical diagnosis, business risk management, abnormal product testing and evaluation. In these cases, the minority classes are usually important. Granular computing has been developed and effectively applied to many problems especially imbalanced data classification. In this paper, we propose a new strategy to build information granulations (IGs) for each class separately and represent sub-attributes based on categorical values (including discretized values of the numerical attributes) to solve the overlapping among IGs. This strategy reduces the computational time, improves classification performance and considers high-balanced accuracy among classes. The experimental results on several datasets have demonstrated the effectiveness of our proposal.