Journal
INFORMATION SCIENCES
Volume 378, Issue -, Pages 282-302Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.03.041
Keywords
Belief functions; Granular computing; Incomplete information tables; Multi-scale decision tables; Rough sets
Categories
Funding
- National Natural Science Foundation of China [61573321, 61272021, 61202206, 61173181, 61322211]
- Zhejiang Provincial Natural Science Foundation of China [LZ12F03002, LY14F030001]
Ask authors/readers for more resources
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 multi-scale decision tables are presented. Finally, knowledge acquisition in the sense of rule induction in consistent and inconsistent incomplete multi-scale decision tables are explored. (C) 2016 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available