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# Research on Imbalanced Classiﬁcation Problem Based on Optimal Random Forest Algorithm

*Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing*, PMLR 245:383-392, 2024.

#### Abstract

In order to solve the binary classiﬁcation problem of imbalanced data, an optimal random forest algorithm GWORF (Grey Wolf Optimizer Random Forest) is pro-posed. The algorithm ﬁrst uses BLSMOTE (BorderLine SMOTE) technology to oversample the imbalanced data set to make the positive and negative data equivalent, and then uses the Grey Wolf optimization algorithm to calculate the optimal parameters, and then puts the calculated optimal parameters into the forest for modeling training. Through testing on four imbalanced data sets, the effectiveness of the GWORF algorithm in the study of imbalanced binary classiﬁcation problems is veriﬁed.