Knowledge Reduction in Incomplete and Vague Objective Information Systems

2012 ◽  
Vol 4 (7) ◽  
pp. 1-9
Author(s):  
Wei Dakuan ◽  
Tang Lujin
Keyword(s):  
Information Systems ◽  
Knowledge Reduction ◽  
Objective Information ◽  
Vague Objective

Knowledge reduction in random information systems via Dempster–Shafer theory of evidence

2005 ◽  
Vol 174 (3-4) ◽  
pp. 143-164 ◽  
Author(s):  
Wei-Zhi Wu ◽  
Mei Zhang ◽  
Huai-Zu Li ◽  
Ju-Sheng Mi
Keyword(s):  
Information Systems ◽  
Knowledge Reduction ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence

ON KNOWLEDGE REDUCTION IN INCONSISTENT DECISION INFORMATION SYSTEMS

2004 ◽  
Vol 12 (05) ◽  
pp. 651-672 ◽  
Author(s):  
DEYU LI ◽  
BO ZHANG ◽  
YEE LEUNG
Keyword(s):  
Information Systems ◽  
Decision Rules ◽  
Compact Representation ◽  
Knowledge Reduction ◽  
Types Of Knowledge ◽  
Prediction Capability

Due to issues such as noise in data, compact representation and prediction capability, many types of knowledge reduction and decision rules have been proposed and applied in inconsistent decision information systems. It is thus important to clarify the interrelationships among the existing types of knowledge reduction. In this paper, the relationships, particularly those suggested in [1], are reconsidered and rectified, and some related results are theoretically improved. In terms of two new types of reducts proposed in this paper together with other existing ones, the method for optimizing all types of decision rules is also discussed in details.

THE ALGORITHM ON KNOWLEDGE REDUCTION IN INCOMPLETE INFORMATION SYSTEMS

2002 ◽  
Vol 10 (01) ◽  
pp. 95-103 ◽  
Author(s):  
JIYE LIANG ◽  
ZONGBEN XU
Keyword(s):  
Information System ◽  
Information Systems ◽  
Incomplete Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Np Hard ◽  
Knowledge Reduction ◽  
Np Hard Problem ◽  
Incomplete Information Systems

Rough set theory is emerging as a powerful tool for reasoning about data, knowledge reduction is one of the important topics in the research on rough set theory. It has been proven that finding the minimal reduct of an information system is a NP-hard problem, so is finding the minimal reduct of an incomplete information system. Main reason of causing NP-hard is combination problem of attributes. In this paper, knowledge reduction is defined from the view of information, a heuristic algorithm based on rough entropy for knowledge reduction is proposed in incomplete information systems, the time complexity of this algorithm is O(|A|2|U|). An illustrative example is provided that shows the application potential of the algorithm.

Rule Acquisition Based on Inconsistent Decision-Making Information System

2013 ◽  
Vol 411-414 ◽  
pp. 1975-1978
Author(s):  
De Xing Wang ◽  
Hong Yan Lu ◽  
Hong Wei Lu
Keyword(s):  
Decision Making ◽  
Information System ◽  
Information Systems ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Knowledge Reduction ◽  
Inconsistent Information ◽  
Rule Acquisition ◽  
Theory Application ◽  
Rules Acquisition

Rule acquisition is a hot topic in the field of data mining. And the inconsistent information systems are widespread nowadays. However, rules acquisition methods are always the difficulty of rough set theory application in inconsistent decision information systems; So the paper proposes a new rule acquisition method. Firstly, we use maximum distribution reduction method for knowledge reduction in single decision-making inconsistent information system and then we use decision-making resolution matrix and decision-making matrix function to get the decision rules. Finally, we mine the rules from inconsistent decision-making information systems.

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