A hierarchical-coevolutionary-MapReduce-based knowledge reduction algorithm with robust ensemble Pareto equilibrium

2016 ◽  
Vol 342 ◽  
pp. 153-175 ◽  
Author(s):  
Weiping Ding ◽  
Jiehua Wang ◽  
Jiandong Wang
Keyword(s):  
Reduction Algorithm ◽  
Knowledge Reduction ◽  
Pareto Equilibrium

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

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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