A simplified approach for data filling in incomplete soft sets. (1st March 2023)

• Record Type: Journal Article
• Authors: Kong, Zhi; Lu, Qiushi; Wang, Lifu; Guo, Ge
• Is Part Of: Expert systems with applications. Volume 213:Part C(2023)
• Publication Date: 2023-03-01
• Subjects: Incomplete soft set -- Filling data -- Unknown data -- Strong association; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119248
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The reasons for the high complexity of the existing approach (DFPAIS) are analyzed. The simplified data filling approach in incomplete soft set (SDFIS) is proposed. The analysis of computational complexity and experiments are performed. Abstract: Data analysis is widely used in various fields, and data is often uncertain, which increases the difficulty of solving problems. Soft set method is a good mathematical tool to deal with uncertain information, but it can't handle unknown data well. The existing approach for data filling in incomplete soft sets can fill data with high accuracy, however it involves a great amount of computation. In this paper a novel approach called simplified approach for data filling in incomplete soft sets (SDFIS) is proposed based on total values of association degrees, which is simpler and easier to understand. The comparison is carried out from several different aspects, and the comparison results show that the proposed approach has low complexity and good scalability. The experiments based on UCI data are performed, and the experiment results show that the proposed approach has almost the same accuracy as the existing approach, but it consumes less time.