Random Balance: Ensembles of variable priors classifiers for imbalanced data

作者：

Highlights：

• Proportions of the classes for each ensemble member are chosen randomly.

• Member training data: sub-sample and over-sample through SMOTE.

• RB-Boost combines Random Balance with AdaBoost.M2.

• Experiments with 86 data sets demonstrate the advantage of Random Balance.

摘要

•Proportions of the classes for each ensemble member are chosen randomly.•Member training data: sub-sample and over-sample through SMOTE.•RB-Boost combines Random Balance with AdaBoost.M2.•Experiments with 86 data sets demonstrate the advantage of Random Balance.

论文关键词：Classifier ensembles,Imbalanced data sets,Bagging,AdaBoost,SMOTE,Undersampling

论文评审过程：Received 4 January 2013, Revised 2 March 2015, Accepted 22 April 2015, Available online 7 May 2015, Version of Record 16 July 2015.

论文官网地址：https://doi.org/10.1016/j.knosys.2015.04.022