Abstract: Resampling is an important method to solve imbalanced data classification problem.However,when the size of data set is very small,undersampling will lose important information of the data set,so oversampling is the research focus of imbalanced data classification.Although the existing oversampling methods partially solve the problem of imbalance between classes,they essentially do not introduce additional information to minority class,and there is still a risk of overfitting.To solve these problems,VTO,an oversampling method based on variance migration of the majority class,is proposed in this paper.In this method,a shift vector is extracted from majority class,and the feature weight matrix of the minority class and the majority class is used for adjustment.Furthermore,the shift vectors filtered by the confidence conditions are superimposed to the center of the minority class,so as to introduce the majority class variance in the generation process of new minority class samples,then enrich the minority class feature space.In order to verify the effectiveness of the proposed algorithm,decision tree is used as classification model to train on 6 KEEL data sets.Compared with SMOTEENN and other over-sampling methods,with F-score and PR-AUC values as evaluation indexes,the results show that VTO is more advantageous in dealing with imbalanced data classification.

Key words: Imbalanced data, Classification, Oversampling, Variance transfer, Covariance