Succinct contrast sets via false positive controlling with an application in clinical process redesign
作者：
Highlights：
• Our method for mining contrast sets (CSs) is 10–77 times faster than the baselines.
• Our method eliminates up to thousands of redundant CSs.
• Our method achieves 1–16% improvements in classification accuracy over the baselines.
• Our method is an efficient way to analyze the length of stay in an emergency department.
摘要
•Our method for mining contrast sets (CSs) is 10–77 times faster than the baselines.•Our method eliminates up to thousands of redundant CSs.•Our method achieves 1–16% improvements in classification accuracy over the baselines.•Our method is an efficient way to analyze the length of stay in an emergency department.
论文关键词：Data mining,Contrast set mining,Classification,False discovery rate,Emergency department,Length of stay (LOS)
论文评审过程：Received 28 January 2019, Revised 14 June 2020, Accepted 14 June 2020, Available online 29 June 2020, Version of Record 8 July 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113670
