An Improved Random Forest Classifier for Imbalanced Learning
Weiping Lin, Jie Gao, Beizhan Wang, Qingqi Hong · 2021
There are many application scenarios involving imbalanced datasets, whereas many traditional machine learning methods have limited ability to adapt to this kind of data. These methods usually have a bias to identify the majority classes while the minority classes are more important in many cases. In this study, we propose a variant of the completely random forest called HCRF. To improve the classification performance of imbalanced data, we introduced 2 mechanisms: random hybrid-resampling and a cost function that focuses on the minority classes. Verified on several imbalanced datasets, HCRF outperforms all comparison methods, demonstrating excellent performance on imbalanced learning.