On rule acquisition in incomplete multi-scale decision tables
Authors: Weizhi Wu, Yuhua Qian, Tongjun Li, Shenming Gu
Abstract:
Granular computing and acquisition of if-then rules are two basic issues in knowledge representation and data mining. A rough set approach to knowledge discovery in incomplete multi-scale decision tables from the perspective of granular computing is proposed in this paper. The concept of incomplete multi-scale information tables in the context of rough sets is first introduced. Information granules at different levels of scales in incomplete multi-scale information tables are then described. Lower and upper approximations with reference to different levels of scales in incomplete multi-scale information tables are also defined and their properties are examined. Optimal scale selection with various requirements in incomplete multi-scale decision tables are further discussed. Relationships among different notions of optimal scales in incomplete multiscale decision tables are presented. Finally, knowledge acquisition in the sense of rule induction in consistent and inconsistent incomplete multi-scale decision tables are explored.
Keywords： Belief functions; Granular computing; Incomplete information tables; Multi-scale decision tables; Rough sets
On rule acquisition in incomplete multi-scale decision tables.pdf
Wed Oct 18 18:55:00 CST 2017