TY - JOUR
T1 - Automatic feature weighting for improving financial Decision Support Systems
AU - Serrano-Silva, Yosimar Oswaldo
AU - Villuendas-Rey, Yenny
AU - Yáñez-Márquez, Cornelio
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/3
Y1 - 2018/3
N2 - 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.
AB - 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.
KW - Bank telemarketing
KW - Banknote authentication
KW - Bankruptcy prediction
KW - Credit risk
KW - Decision support
KW - Feature weight
UR - http://www.scopus.com/inward/record.url?scp=85041330925&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2018.01.005
DO - 10.1016/j.dss.2018.01.005
M3 - Artículo
SN - 0167-9236
VL - 107
SP - 78
EP - 87
JO - Decision Support Systems
JF - Decision Support Systems
ER -