关键词: 随机森林, SMOTE算法, 不平衡数据集

Abstract: There are dataset marginal distribution problem and the computational complexity shortcomings using random forest combined SMOTE algorithm in dealing with imbalanced dataset. This paper proposes a TSMOTE algorithm （triangle SMOTE） and MDSMOTE algorithm （Max Distance SMOTE）. The core idea of the improved algorithm is to restrict the generation  of new samples in a certain area, so that the distribution of the sample set tends to be centralized, which reduces the complexity of the traditional SMOTE algorithm and the time complexity of the algorithm. Extensive experiments on six imbalanced datasets show that the improved algorithm reduces the time consumption and achieves higher G-mean value, F-value value, AUC value compared with the state-of-art method SMOTE.

Key words: random forest, SMOTE algorithm, imbalanced dataset