A Heuristic Genetic Algorithm for Continuous Attribute Discretization in Rough Set Theory

2011 ◽  
Vol 211-212 ◽  
pp. 132-136 ◽  
Author(s):  
Zhao Hui Ren ◽  
Yuan Hao ◽  
Bang Chun Wen
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision System ◽  
Minimum Number ◽  
Cut Set ◽  
Continuous Attribute ◽  
Decision Tables

Continuous attribute discretization based on rough set is to got possibly minimum number of cuts, and at the same time it should not weaken the indiscernibility ability of the original decision system. In order to obtain the optimal cut set of the continuous attribute system, based on research the choice of candidate cut set, this paper presents a heuristic genetic algorithm for continuous attribute discretization to decision tables. In this algorithm making the importance of the continuous cut as heuristic message, a new operator is constructed to not only maintain the discernibility of the cuts selected, but also improve local search ability of the algorithm. Compared the performance of this method with the others’, this method is proved effective and superiority.

Fragile Watermarking of Decision System Using Rough Set Theory

2018 ◽  
Vol 43 (12) ◽  
pp. 7621-7633 ◽  
Author(s):  
Vidhi Khanduja ◽  
Shampa Chakraverty
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Fragile Watermarking ◽  
Decision System

scholarly journals Attribute Reduction Based on Consistent Covering Rough Set and Its Application

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jianchuan Bai ◽  
Kewen Xia ◽  
Yongliang Lin ◽  
Panpan Wu
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Processing Step ◽  
Important Processing ◽  
Continuous Attribute ◽  
Covering Rough Set

As an important processing step for rough set theory, attribute reduction aims at eliminating data redundancy and drawing useful information. Covering rough set, as a generalization of classical rough set theory, has attracted wide attention on both theory and application. By using the covering rough set, the process of continuous attribute discretization can be avoided. Firstly, this paper focuses on consistent covering rough set and reviews some basic concepts in consistent covering rough set theory. Then, we establish the model of attribute reduction and elaborate the steps of attribute reduction based on consistent covering rough set. Finally, we apply the studied method to actual lagging data. It can be proved that our method is feasible and the reduction results are recognized by Least Squares Support Vector Machine (LS-SVM) and Relevance Vector Machine (RVM). Furthermore, the recognition results are consistent with the actual test results of a gas well, which verifies the effectiveness and efficiency of the presented method.

scholarly journals Converse approximation and rule extraction from decision tables in rough set theory

2008 ◽  
Vol 55 (8) ◽  
pp. 1754-1765 ◽  
Author(s):  
Yuhua Qian ◽  
Jiye Liang ◽  
Chuangyin Dang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Rule Extraction ◽  
Decision Tables
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