Knowledge Reduction of Rough Set Based on Partition

2005 ◽  
pp. 48-55
Author(s):  
Xiaobing Pei ◽  
Yuanzhen Wang
Keyword(s):  
Rough Set ◽  
Knowledge Reduction

Rough Set Classification Algorithms Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3119-3122
Author(s):  
Yan Xue Dong ◽  
Fu Hai Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Texture Classification ◽  
Texture Feature ◽  
Decision Table ◽  
Classification Algorithms ◽  
Reduction Algorithm ◽  
Discrete Method ◽  
Knowledge Reduction

The basic theory of rough set is given and a method for texture classification is proposed. According to the GCLM theory, texture feature is extracted and generate 32 feature vectors to form a decision table, find a minimum set of rules for classification after attribute discretization and knowledge reduction, experimental results show that using rough set theory in texture classification, accompanied by appropriate discrete method and reduction algorithm can get better classification results

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.

A Prediction Method of Power Energy Saving Potential Based on Rough Set Neural Network

2010 ◽  
Vol 44-47 ◽  
pp. 3795-3799
Author(s):  
Jin Ying Li ◽  
Ya Jun Wei ◽  
Jin Chao Li ◽  
Yu Zhi Zhao
Keyword(s):  
Neural Network ◽  
Energy Saving ◽  
Rough Set ◽  
Reduction Method ◽  
Prediction Method ◽  
Pollutant Emission ◽  
Resource Saving ◽  
Environment Friendly ◽  
Knowledge Reduction ◽  
Energy Saving Potential

Power industry is the key field of implementing energy saving and pollutant emission reduction in china, strengthen power energy saving is helpful to establish a resource-saving and environment-friendly society and promote a sustainable development of economic society. This paper synchronizes respective advantages of rough set and neural network, puts forward a prediction model-RSBPNN which uses rough set knowledge reduction method to prune the redundant and neural network to build a forecasting model.

scholarly journals Mechanical Product Ecological Design Knowledge Reduction Based on Rough Set

Procedia CIRP ◽  
2019 ◽  
Vol 80 ◽  
pp. 33-38 ◽  
Author(s):  
Lei Zhang ◽  
Zhifeng Jin ◽  
Yu Zheng ◽  
Rui Jiang
Keyword(s):  
Rough Set ◽  
Design Knowledge ◽  
Ecological Design ◽  
Knowledge Reduction

A rough set approach to knowledge reduction based on inclusion degree and evidence reasoning theory

2003 ◽  
Vol 20 (5) ◽  
pp. 298-304 ◽  
Author(s):  
Mei Zhang ◽  
Li Da Xu ◽  
Wen-Xiu Zhang ◽  
Huai-Zu Li
Keyword(s):  
Rough Set ◽  
Knowledge Reduction ◽  
Inclusion Degree
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