A Heuristic Knowledge Reduction Algorithm for Real Decision Formal Contexts

2012 ◽  
pp. 303-312 ◽  
Author(s):  
Jinhai Li ◽  
Changlin Mei ◽  
Yuejin Lv ◽  
Xiao Zhang
Keyword(s):  
Reduction Algorithm ◽  
Heuristic Knowledge ◽  
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

A Heuristic Reduction Algorithm Based on the Feature Weight

2014 ◽  
Vol 1070-1072 ◽  
pp. 2051-2055
Author(s):  
Xiao Xue Xing ◽  
Li Min Du ◽  
Wei Wei Shang
Keyword(s):  
Attribute Reduction ◽  
Reduction Algorithm ◽  
Discernibility Matrix ◽  
Heuristic Knowledge ◽  
Feature Weight ◽  
The Mean ◽  
Mean Time

The basic attribute reduction algorithm based on discernibility matrix was introduced. Through analyzing the algorithm, the shortages were found. Then the heuristic reduction algorithm based on the feature weight is presented in the paper. In the algorithm, the discernibility matrix and the heuristic knowledge are combined toghther. It can be proved that the proposed algorithm is more intuitive and easier in computation. At the mean time the speed of the reduction algorithm could be improved.

Reduction of fuzzy-crisp concept lattice based on order-class matrix

2020 ◽  
Vol 39 (5) ◽  
pp. 8001-8013
Author(s):  
Yidong Lin ◽  
Jinjin Li ◽  
Shujiao Liao ◽  
Jia Zhang ◽  
Jinghua Liu
Keyword(s):  
Data Mining ◽  
Concept Lattice ◽  
Attribute Reduction ◽  
Reduction Algorithm ◽  
Fuzzy Decision ◽  
Knowledge Reduction ◽  
Irreducible Elements ◽  
Proposed Model ◽  
Critical Problems ◽  
New Framework

Knowledge reduction is one of critical problems in data mining and information processing. It can simplify the structure of the lattice during the construction of fuzzy-crisp concept lattice. In terms of fuzzy-crisp concept, we develop an order-class matrix to represent extents and intents of concepts, respectively. In order to improve the computing efficiency, it is necessary to reduce the size of lattices as much as possible. Therefore the judgement theorem of meet-irreducible elements is proposed. To deal with attribute reductions, we develop a discernibility Boolean matrix in formal fuzzy contexts by preserving extents of meet-irreducible elements via order-class matrix. A heuristic attribute-reduction algorithm is proposed. Then we extend the proposed model to consistent formal fuzzy decision contexts. Our methods present a new framework for knowledge reduction in formal fuzzy contexts.

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