Abstract: The problem of imbalanced data is prevalent in various applications of big data and machine learning,like medical diagnosis and abnormal detection.Researchers have proposed or used a number of methods for imbalanced learning,including data sampling(e.g.SMOTE) and ensemble learning(e.g.EasyEnsemble) methods.The oversamp-ling methods in data sampling may have problems such as over-fitting or low classification accuracy of boundary samples,while the under-sampling methods may lead to under-fitting.The Rotation SMOTE algorithm was proposed in this paper incorporating the basic idea of SMOTE,Bagging,Boosting and other algorithms,and SMOTE was used to indirectly increase the weight of minority samples based on the prediction result of the base classifier in the Boosting process.According to the basic idea of Focal Loss,this paper proposed FocalBoost algorithm that directly optimizes the sample weight updating strategy of AdaBoost based on the prediction results of the base classifier.Based on the experiment with multiple evaluation metrics on 11 unbalanced data sets in different application fields,Rotation SMOTE can obtain the highest recall score on all datasets compared with other imbalanced data learning algorithms (including SMOTEBoost and EasyEnsemble),and achieves the best or the second best G-means and F1Score on most datasets,while FocalBoost achieves better performance on 9 of these unbalanced datasets compared to the original AdaBoost.

Key words: Boosting, Ensemble learning, Imbalanced data, SMOTE