Research on Diagnosis Method Based on Multi-Class Sample Imbalanced Data

In reality, data on many engineering problems are classified as imbalanced data, which poses significant challenges to adopting a data-driven approach. In this paper, the SOU data preprocessing method combining synthetic minority oversampling technique, random oversampling and random under sampling is used for data equalization. After, the cars evaluation, wine production regions are classified using the ensemble learning based on stacking method. Research is also conducted on the classification of imbalances in the condition assessment of aero-engines at work. The results show that the data equalization method and diagnostic framework proposed in this paper can effectively diagnose multi-class imbalanced data, and the ensemble model has higher diagnostic stability and generalization ability, which is more effective for solving multi-class imbalance problems.