An Algorithm of Attribute Reduction Based on Granular Computing in Manufacturing Grid System

2016 ◽  
Author(s):  
Special Issues Editor
Keyword(s):  
Granular Computing ◽  
Attribute Reduction ◽  
Grid System ◽  
Manufacturing Grid

The Incremental Knowledge Acquisition Based on Hash Algorithm

2016 ◽  
Vol 24 (03) ◽  
pp. 347-366 ◽  
Author(s):  
Qing-Hua Zhang ◽  
Long-Yang Yao ◽  
Guan-Sheng Zhang ◽  
Yu-Ke Xin
Keyword(s):  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Algorithm Analysis ◽  
Data Sets ◽  
Hash Algorithm ◽  
Acquisition Method

In this paper, a new incremental knowledge acquisition method is proposed based on rough set theory, decision tree and granular computing. In order to effectively process dynamic data, describing the data by rough set theory, computing equivalence classes and calculating positive region with hash algorithm are analyzed respectively at first. Then, attribute reduction, value reduction and the extraction of rule set by hash algorithm are completed efficiently. Finally, for each new additional data, the incremental knowledge acquisition method is proposed and used to update the original rules. Both algorithm analysis and experiments show that for processing the dynamic information systems, compared with the traditional algorithms and the incremental knowledge acquisition algorithms based on granular computing, the time complexity of the proposed algorithm is lower due to the efficiency of hash algorithm and also this algorithm is more effective when it is used to deal with the huge data sets.

Construction and Application of Hierarchical Knowledge Granularity

2010 ◽  
Vol 143-144 ◽  
pp. 717-721
Author(s):  
Chun Feng Liu ◽  
Li Feng
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Computational Complexity ◽  
Granular Computing ◽  
Attribute Reduction ◽  
Optimization Theory ◽  
Hierarchy Analysis ◽  
Speed Up ◽  
Construction Algorithms ◽  
Knowledge Granularity

As one aspect of granular computing, hierarchical knowledge granularity can speed up solution, and reduce computational complexity. This paper describes the structure and hierarchy analysis of granularity simply, details the current methods of construction algorithms in granular computing, and emphasizes the performance comparisons of various construction algorithms, and finally reviews the applications of knowledge granularity in rule extraction, attribute reduction, cluster analysis, optimization theory, neural network and fuzzy control and so on.

scholarly journals An Improved Attribute Reduction Algorithm based on Granular Computing

2015 ◽  
Vol 10 (6) ◽  
pp. 96
Author(s):  
Xiao Tang ◽  
Lan Shu
Keyword(s):  
Granular Computing ◽  
Reduction Method ◽  
No Reduction ◽  
Attribute Reduction ◽  
Important Application ◽  
Experimental Result ◽  
Intelligent Computing ◽  
Actual Application ◽  
Application Data ◽  
Computing Method

Granular computing is a new intelligent computing method based on problem solving, information processing and pattern classification. Granular com- puting based attribute reduction method is an important application of Granular computing. These algorithms are mostly based on reduction core. However, some information systems may have no reduction core, especially in the actual application data. For this case, those algorithms are powerless. In this paper, an improved reduc- tion algorithm based on granular computing is proposed. The algorithm is validated by the experimental result.

scholarly journals Multigranulations Rough Set Method of Attribute Reduction in Information Systems Based on Evidence Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Minlun Yan
Keyword(s):  
Information Systems ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Rules ◽  
Evidence Theory ◽  
Point Of View ◽  
Upper Approximation

Attribute reduction is one of the most important problems in rough set theory. However, from the granular computing point of view, the classical rough set theory is based on a single granulation. It is necessary to study the issue of attribute reduction based on multigranulations rough set. To acquire brief decision rules from information systems, this paper firstly investigates attribute reductions by combining the multigranulations rough set together with evidence theory. Concepts of belief and plausibility consistent set are proposed, and some important properties are addressed by the view of the optimistic and pessimistic multigranulations rough set. What is more, the multigranulations method of the belief and plausibility reductions is constructed in the paper. It is proved that a set is an optimistic (pessimistic) belief reduction if and only if it is an optimistic (pessimistic) lower approximation reduction, and a set is an optimistic (pessimistic) plausibility reduction if and only if it is an optimistic (pessimistic) upper approximation reduction.

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