An Improved Ensemble Learning for Imbalanced Data Classification
Zhengwu Yuan, Pu Zhao · 2019
In the field of data mining, imbalanced data is widespread, and ensemble learning algorithms are a classifier that is more effective in classifying imbalanced data. However, the ensemble learning algorithm itself is not optimized for imbalanced data. Therefore, an imbalanced data processing method based on data level of ensemble learning SE-gcForest is proposed. This method introduces the data level processing idea after the Multi-grained Scanning window process of the gcForest algorithm. The SMOTE algorithm and the EasyEnsemble idea deal with imbalanced data. Experiments show that this method is more effective when the data imbalanced ratio is lower and higher.