Knowledge Reduction Based on Rough Entropy in Inconsistent Systems

Author

Li, Jian ; Xu, Xiaojing

Author_Institution

Sch. of Math. & Syst. Sci., Shandong Univ., Jinan

fYear

2007

fDate

Aug. 30 2007-Sept. 1 2007

Firstpage

355

Lastpage

360

Abstract

Based on conditional rough entropy theory, the concepts of rough entropy of elements and decision sets in decision information systems are given. The relationships between conditional rough entropy and alternative types of knowledge reduction in inconsistent systems are investigated. The approaches to look for distribution reduction, possible reduction (upper approximation reduction) and lower approximation reduction are given. Finally, an instance is solved, which verifies the validity of the approaches.

Keywords

data mining; data reduction; decision support systems; decision theory; entropy; knowledge representation; rough set theory; conditional rough entropy theory; data mining; decision information systems; decision set; distribution reduction; inconsistent systems; knowledge discovery; knowledge reduction; lower approximation reduction; possible reduction; upper approximation reduction; Data mining; Decision making; Entropy; Information systems; Mathematics; Pattern recognition; Rough sets; Set theory; Uncertainty; Virtual colonoscopy;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Language Processing and Knowledge Engineering, 2007. NLP-KE 2007. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-1610-3

Electronic_ISBN

978-1-4244-1611-0

Type

conf

DOI

10.1109/NLPKE.2007.4368055

Filename

4368055