Title :
Knowledge Reduction Based on Rough Entropy in Inconsistent Systems
Author :
Li, Jian ; Xu, Xiaojing
Author_Institution :
Sch. of Math. & Syst. Sci., Shandong Univ., Jinan
fDate :
Aug. 30 2007-Sept. 1 2007
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;
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
DOI :
10.1109/NLPKE.2007.4368055
