Abstract: When dealing with imbalanced data,traditional classifiers tend to guarantee the accuracy of the majority class and sacrifice the accuracy of the minority class,resulting in a higher error rate of the minority class.Aiming at this problem,an improved XGBoost method for binary imbalanced data is proposed.The main idea is to improve the characters of imbalanced data from three levels,data,features,and algorithms.Firstly,at the data level,Conditional Generative Adversarial Nets (CGAN) learns the distributive information of minority samples and then trains the generator to generate a few supple-mentary samples to adjust the imbalance of the data.Secondly,at the feature level,it uses XGBoost for feature combination to generate new features,and then uses the minimal Redundancy-Maximal Relevance (mRMR) algorithm to screen out a subset of features that are more suitable for imbalanced data classification.Finally,at the algorithm level,it introduces a Focal Loss function for imbalanced data classification to improve XGBoost.The improved XGBoost is trained on the new dataset to obtain the final model.In the experimental stage,G-mean and AUC are selected as the evaluation indicators.The experimental results on 6 sets of KEEL datasets verify the feasibility of the proposed improved method.At the same time,the method is compared with the existing four imbalanced classification models.The experimental results show that the proposed improved method has better classification effect.

Key words: CGAN, Feature combination, Focal Loss, Imbalanced data, mRMR, XGBoost