scholarly journals A Fast Attribute Reduction Algorithm Based on a Positive Region Sort Ascending Decision Table

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1189 ◽  
Author(s):  
Linzi Yin ◽  
Zhaohui Jiang
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Experimental Tests ◽  
Decision Table ◽  
The Novel ◽  
Reduction Algorithm ◽  
Positive Region ◽  
Efficient Reduction ◽  
Key Steps ◽  
High Computational Efficiency

Attribute reduction is one of the challenging problems in rough set theory. To accomplish an efficient reduction algorithm, this paper analyzes the shortcomings of the traditional methods based on attribute significance, and suggests a novel reduction way where the traditional attribute significance calculation is replaced by a special core attribute calculation. A decision table called the positive region sort ascending decision table (PR-SADT) is defined to optimize some key steps of the novel reduction method, including the special core attribute calculation, positive region calculation, etc. On this basis, a fast reduction algorithm is presented to obtain a complete positive region reduct. Experimental tests demonstrate that the novel reduction algorithm achieves obviously high computational efficiency.

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.

Positive Region Reduct Based on Relative Discernibility and Acceleration Strategy

2018 ◽  
Vol 26 (04) ◽  
pp. 521-551 ◽  
Author(s):  
Hao Ge ◽  
Chuanjian Yang ◽  
Longshu Li
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Reduction Process ◽  
High Volume ◽  
Decision Table ◽  
High Dimensional ◽  
Classical Type ◽  
Data Sets ◽  
Radix Sort ◽  
Positive Region

Attribute reduction is one of key issues in rough set theory, and positive region reduct is a classical type of reducts. However, a lot of reduction algorithms have more high time expenses when dealing with high-volume and high-dimensional data sets. To overcome this shortcoming, in this paper, a relative discernibility reduction method based on the simplified decision table of the original decision table is researched for obtaining positive region reduct. Moreover, to further improve performance of reduction algorithm, we develop an accelerator for attribute reduction, which reduces the radix sort times of the reduction process to raise algorithm efficiency. By the accelerator, two positive region reduction algorithms, i.e., FARA-RS and BARA-RS, based on the relative discernibility are designed. FARA-RS simultaneously reduce the size of the universe and the number of radix sort to achieve speedup and BARA-RS only reduce the number of radix sort to achieve acceleration. The experimental results show that the proposed reduction algorithms are effective and feasible for high dimensional and large data sets.

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.

Research of Improved Attribute Reduction Algorithm Based on Data Mining of Rough Set

2014 ◽  
Vol 644-650 ◽  
pp. 2120-2123 ◽  
Author(s):  
De Zhi An ◽  
Guang Li Wu ◽  
Jun Lu
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Reduction Algorithm ◽  
The Core ◽  
Rules Extraction

At present there are many data mining methods. This paper studies the application of rough set method in data mining, mainly on the application of attribute reduction algorithm based on rough set in the data mining rules extraction stage. Rough set in data mining is often used for reduction of knowledge, and thus for the rule extraction. Attribute reduction is one of the core research contents of rough set theory. In this paper, the traditional attribute reduction algorithm based on rough sets is studied and improved, and for large data sets of data mining, a new attribute reduction algorithm is proposed.

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.

Research to E-Commerce Customers Losing Predict Based on Rough Set

2011 ◽  
Vol 58-60 ◽  
pp. 164-170 ◽  
Author(s):  
Ming Jun Wang ◽  
Shu Xian Deng
Keyword(s):  
Data Analysis ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Customer Behavior ◽  
Purchase Behavior ◽  
Decision Table ◽  
Purchasing Behavior ◽  
Expert Opinions ◽  
Loss Prediction

The present paper based on rough set theory is to analyze the reason of an e-commerce customers losing. The e-commerce is virtual, customers purchase behavior is random, and there is the 20/80 theory. The focus to the e-commerce customers losing predict is to bring enterprise 80percent profits or frequent buying clients, they will be the study samples. Therefore, we must first find out these clients from numerous customers, analyze their purchasing behavior, and it is one of the important links loss prediction. This process may be realized by customer behavior data clustering. We have analyzed the data in one e-commerce database, and according to a certain algorithm has classified these customers, one kind is superior customers, one kind is general customers, the rest is temporary customers. And a lot of questionnaire survey have been done to these kinds of customers, and then combining e-commerce expert opinions formed the customers data analysis and decision table, then the algorithm, which is the decision table blindly delete attribute reduction algorithm, is adopted to process the attributes reduction to the decision table. Then, we get the reduction table of the customers’ data analysis and decision. According to the reduction table, we summarize e-commerce customers’ loss decision rule. Through these decision-making rules, we can predict these losing customers, and take timely measures necessary to retain.

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