Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction

作者：

Highlights：

• We examined the effectiveness an optimized cluster-based undersampling technique.

• We used a GA-based optimization approach for selecting the appropriate instances.

• A critical issue of real-world knowledge extraction is the data imbalance problem.

• The proposed method is successfully applied to the bankruptcy prediction problem.

摘要

•We examined the effectiveness an optimized cluster-based undersampling technique.•We used a GA-based optimization approach for selecting the appropriate instances.•A critical issue of real-world knowledge extraction is the data imbalance problem.•The proposed method is successfully applied to the bankruptcy prediction problem.

论文关键词：Genetic algorithms,Cluster-based undersampling technique,Imbalance data,Corporate bankruptcy prediction

论文评审过程：Received 2 December 2015, Revised 12 March 2016, Accepted 21 April 2016, Available online 23 April 2016, Version of Record 6 May 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.04.027