A Robust profit measure for binary classification model evaluation
作者：
Highlights：
• We present a measure for profit-driven evaluation of models in the presence of strong variability.
• This variability may come from fixed effects, fixed distributions, and random shocks.
• The measure was tested both in a synthetic case and an empirical case.
• The measure outperforms other commonly used ones in highly variable environments.
• The measure allows selecting the most profitable model in the long run.
摘要
•We present a measure for profit-driven evaluation of models in the presence of strong variability.•This variability may come from fixed effects, fixed distributions, and random shocks.•The measure was tested both in a synthetic case and an empirical case.•The measure outperforms other commonly used ones in highly variable environments.•The measure allows selecting the most profitable model in the long run.
论文关键词：Supervised binary classification,Business analytics,Performance measures,Profit-driven analytics
论文评审过程：Received 27 February 2017, Revised 16 September 2017, Accepted 17 September 2017, Available online 19 September 2017, Version of Record 26 September 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.045
