Feature selection for continuous aggregate response and its application to auto insurance data
作者：
Highlights：
• We study major difficulties in aggregate data analysis and propose a solution for it.
• The proposed group feature selection algorithm is simple, fast, and flexible.
• The proposed approach is applied to real data to demonstrate its practical effectiveness.
• Important rating factors for risk assessment in auto insurance are identified.
摘要
•We study major difficulties in aggregate data analysis and propose a solution for it.•The proposed group feature selection algorithm is simple, fast, and flexible.•The proposed approach is applied to real data to demonstrate its practical effectiveness.•Important rating factors for risk assessment in auto insurance are identified.
论文关键词：Aggregate data,Feature selection,Auto insurance,Tariff classification,Risk assessment
论文评审过程：Received 29 May 2017, Revised 15 September 2017, Accepted 1 October 2017, Available online 4 October 2017, Version of Record 14 October 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.10.007
