SMOTE类算法研究综述OA北大核心CSTPCD

Synthetic minority oversampling technique(SMOTE)has become one of the mainstream methods for dealing with unbalanced data due to its ability to effectively deal with minority samples,and many SMOTE im-provement algorithms have been proposed,but very little research existing considers popular algorithmic-level im-provement methods.Therefore a more comprehensive analysis of existing SMOTE class algorithms is provided.Firstly,the basic principles of the SMOTE method are elaborated in detail,and then the SMOTE class algorithms are systematically analyzed mainly from the two levels of data level and algorithmic level,and the new ideas of the hybrid improvement of data level and algorithmic level are introduced.Data-level improvement is to balance the data distribution by deleting or adding data through different operations during preprocessing;algorithmic-level improve-ment will not change the data distribution,and mainly strengthens the focus on minority samples by modifying or creating algorithms.Comparison between these two kinds of methods shows that,data-level methods are less re-stricted in their application,and algorithmic-level improvements generally have higher algorithmic robustness.In order to provide more comprehensive basic research material on SMOTE class algorithms,this paper finally lists the com-monly used datasets,evaluation metrics,and gives ideas of research in the future to better cope with unbalanced data problem.