The effect of feature selection on financial distress prediction
作者：
Highlights：
• Filter and wrapper based feature selection are employed for financial distress prediction.
• In addition, two bankruptcy prediction datasets and two credit scoring datasets are used.
• On average, GA and LR perform better over the credit and bankruptcy datasets respectively.
• However, there is no exact winner over the four datasets.
• In addition, performing feature selection does not always improve the models’ performance.
摘要
•Filter and wrapper based feature selection are employed for financial distress prediction.•In addition, two bankruptcy prediction datasets and two credit scoring datasets are used.•On average, GA and LR perform better over the credit and bankruptcy datasets respectively.•However, there is no exact winner over the four datasets.•In addition, performing feature selection does not always improve the models’ performance.
论文关键词：Financial distress prediction,Bankruptcy prediction,Credit scoring,Feature selection,Data mining
论文评审过程：Received 17 December 2013, Revised 7 September 2014, Accepted 11 October 2014, Available online 16 October 2014.
论文官网地址：https://doi.org/10.1016/j.knosys.2014.10.010
