Review of Classification Methods on Unbalanced Data Sets

This paper studies the classification of unbalanced data sets. First, this kind of data sets is briefly introduced, and then the classification methods of unbalanced data sets are analyzed in detail from different perspectives such as data sampling method, algorithm level, feature level, cost-sensitive function, and deep learning. In addition, the data sampling methods are divided into different technologies for introduction: unbalanced data set classification method based on synthetic minority over-sampling technology (SMOTE), support vector machine (SVM) technology, and k-nearest neighbor (KNN) technology, etc. Then, the advantages and disadvantages of these methods are compared. Finally, the evaluation criteria of the unbalanced data set classifier are summarized, and the future work directions are prospected and summarized.

Automated summary

This paper explores the classification of unbalanced data sets, analyzing various methods from data sampling, algorithm, feature, cost-sensitive function, and deep learning perspectives, comparing the advantages and disadvantages of different techniques, and outlining future research directions.