Granule, similarity and decision table of knowledge reduction algorithm

Author(s):  
He Lin ◽  
Pingji Zou
2013 ◽  
Vol 347-350 ◽  
pp. 3119-3122
Author(s):  
Yan Xue Dong ◽  
Fu Hai Huang

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


2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.


2012 ◽  
Vol 457-458 ◽  
pp. 1230-1234 ◽  
Author(s):  
Ying He ◽  
Dan He

A discernibility matrix-based attribute reduction algorithm of decision table is introduced in this paper, which takes the importance of attributes as the heuristic message. This method solves the problem of the attribute selection when the frequencies of decision table attributes are equal. The result shows that this method can give out simple but effective method of attribute reduction.


2014 ◽  
Vol 533 ◽  
pp. 237-241
Author(s):  
Xiao Jing Liu ◽  
Wei Feng Du ◽  
Xiao Min

The measure of the significance of the attribute and attribute reduction is one of the core content of rough set theory. The classical rough set model based on equivalence relation, suitable for dealing with discrete-valued attributes. Fuzzy-rough set theory, integrating fuzzy set and rough set theory together, extending equivalence relation to fuzzy relation, can deal with fuzzy-valued attributes. By analyzing three problems of FRAR which is a fuzzy decision table attribute reduction algorithm having extensive use, this paper proposes a new reduction algorithm which has better overcome the problem, can handle larger fuzzy decision table. Experimental results show that our reduction algorithm is much quicker than the FRAR algorithm.


2010 ◽  
Vol 171-172 ◽  
pp. 154-158 ◽  
Author(s):  
Wen Hao Shu ◽  
Zhang Yan Xu ◽  
Shen Ruan

At present, some scholars have provided the attribution reduction algorithms of incomplete decision table. The time complexity of many algorithms are .To cut down the time complexity of the algorithms for computing attribution reduction , the definition of discernibility matrix based on positive region and the corresponding definition of the attribution reduction are provided. At the same time, it is proved that the attribution reduction is equivalent to the attribution reduction based on the positive region. The discernibility matrix is simplified for not comparing the objects between .On this condition, a efficient algorithm for computing attribution reduction is designed with the simplified discernibility matrix, whose time complexity is .At last, an emulate example is used to illustrate the efficiency of the new algorithm.


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