Automatic feature weighting for improving financial Decision Support Systems
作者：
Highlights：
• We propose a novel methodology for improving DSS through automatic feature weighting.
• We show that automatic feature weighting leads to an improvement in decision-making.
• Naïve Associative Classifier performance was improved by the proposed methodology.
• The statistical analysis shows that metaheuristics are good for feature weighting.
• Differential Evolution is adequate for decision-making having low computational cost.
摘要
We propose a novel methodology for improving financial Decision Support Systems (DSS) through automatic feature weighting. Using this methodology, we show that automatic feature weighting leads to a significant improvement in the performance of decision-making algorithms over financial data, which are the key of financial DSS. The statistical analysis carried out shows that metaheuristic algorithms are good for automatic feature weighting, and that Differential Evolution (DE) offers a good trade-off between decision-making performance and computational cost. We believe these results contribute to the development of novel financial DSS.
论文关键词：Credit risk,Bankruptcy prediction,Banknote authentication,Bank telemarketing,Feature weight,Decision support
论文评审过程：Received 11 September 2017, Revised 23 January 2018, Accepted 24 January 2018, Available online 31 January 2018, Version of Record 6 March 2018.
论文官网地址：https://doi.org/10.1016/j.dss.2018.01.005
