An Improved Over-sampling Algorithm based on iForest and SMOTE
Yifeng Zheng, Guohe Li, Teng Zhang · 2019
Imbalance learning is one of the most challenging problems in supervised learning, so many different strategies are designed to tackle balanced sample distribution. The over-sampling techniques which achieve a relatively balanced class distribution through synthesizing samples receive more and more attention. In this paper, we present an over-sampling approach based on isolation Forest (iForest) and SMOTE, called iForest-SMOTE. Firstly, for minority class samples, iForest-score is employed to assess the importance of each sample based on iForest model. Then, in each SMOTE process, roulette wheel selection based on iForest-score is utilized to select the neighbor sample. Finally, M-dimensional-sphere interpolation approach is employed to generate a new sample. The experiments illustrate that our approach takes into account the spatial distribution of minority class samples and sample synthetic simultaneously. Therefore, iForest-SMOTE can effectively improve the performance of the classification model.