Double-quantitative distance measurement and classification learning based on the tri-level granular structure of neighborhood system
作者：
Highlights：
• Size valuation and logical operation are added for neighborhood swarms and libraries.
• Double-quantitative and tri-level distances are offered for hierarchical measurement.
• Double-quantitative classifier is designed to gain better classification performance.
• Tri-level granular structure of neighborhood system is fully perfected and extended.
摘要
•Size valuation and logical operation are added for neighborhood swarms and libraries.•Double-quantitative and tri-level distances are offered for hierarchical measurement.•Double-quantitative classifier is designed to gain better classification performance.•Tri-level granular structure of neighborhood system is fully perfected and extended.
论文关键词：Neighborhood rough sets,Granular computing,Tri-level granular structure,Double quantification,Distance measurement,Machine learning
论文评审过程：Received 21 March 2020, Revised 18 January 2021, Accepted 19 January 2021, Available online 23 January 2021, Version of Record 11 February 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.106799
