Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction

作者：

Highlights：

• We propose geometric mean based boosting algorithm (GMBoost).

• We propose GMBoost to resolve data imbalance problem.

• GMBoost considers geometric mean of error rates of majority and minority classes.

• We experiment GMBoost, AdaBoost and cost-sensitive boosting on bankruptcy prediction.

• The comparative results shows GMBoost outperforms in imbalanced and balanced data.

摘要

•We propose geometric mean based boosting algorithm (GMBoost).•We propose GMBoost to resolve data imbalance problem.•GMBoost considers geometric mean of error rates of majority and minority classes.•We experiment GMBoost, AdaBoost and cost-sensitive boosting on bankruptcy prediction.•The comparative results shows GMBoost outperforms in imbalanced and balanced data.

论文关键词：Data imbalance,Bankruptcy prediction,Over-sampling,SMOTE,Cost-sensitive boosting,AdaBoost,GMBoost

论文评审过程：Available online 16 September 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.08.025