Research on Heuristic Knowledge Reduction Algorithm for Incomplete Decision Table

2010 ◽  
Author(s):  
Xiaopeng Dai ◽  
Dahong Xiong
Keyword(s):  
Decision Table ◽  
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 Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

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.

Discernibility Matrix-Based Attribute Reduction Algorithm of Decision Table

2012 ◽  
Vol 457-458 ◽  
pp. 1230-1234 ◽  
Author(s):  
Ying He ◽  
Dan He
Keyword(s):  
Attribute Reduction ◽  
Attribute Selection ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Discernibility Matrix

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.

Fuzzy Attribute Reduction Based on Fuzzy Similarity

2014 ◽  
Vol 533 ◽  
pp. 237-241
Author(s):  
Xiao Jing Liu ◽  
Wei Feng Du ◽  
Xiao Min
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Fuzzy Decision ◽  
Core Content ◽  
Fuzzy Similarity

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.

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.

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