A New Method for Constructing Decision Tree Based on Rough Set Theory

2007 ◽  
Author(s):  
Longjun Huang ◽  
Minghe Huang ◽  
Bin Guo ◽  
Zhiming Zhang
Keyword(s):  
Decision Tree ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
New Method

Optimization Analysis on Dynamic Reduction Algorithm

2018 ◽  
Vol 6 (5) ◽  
pp. 447-458
Author(s):  
Yizhou Chen ◽  
Jiayang Wang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Reduction Process ◽  
New Method ◽  
Reduction Algorithm ◽  
Optimization Analysis ◽  
Sampling Process ◽  
Dynamic Methods ◽  
Definition Of

Abstract On the basis of rough set theory, the strengths of dynamic reduction are elaborated compared with traditional non-dynamic methods. A systematic concept of dynamic reduction from sampling process to the generation of the reduct set is presented. A new method of sampling is created to avoid the defects of being too subjective. And in order to deal with the over-sized time consuming problem in traditional dynamic reduction process, a quick algorithm is proposed within the constraint conditions. We have also proved that dynamic core possesses the essential characteristics of a reduction core on the basis of the formalized definition of the multi-layered dynamic core.

New method for discretization of continuous attributes in rough set theory

2010 ◽  
Vol 21 (2) ◽  
pp. 250-253 ◽  
Author(s):  
Rong Cang ◽  
Xiukun Wang ◽  
Kai Li ◽  
Nanhai Yang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
New Method

A New Method for Decision Tree Based Discernibility Matrix and Degree of Consistent Dependence

2015 ◽  
Vol 743 ◽  
pp. 390-394
Author(s):  
Liu Liu ◽  
Bao Sheng Wang ◽  
Qiu Xi Zhong ◽  
Hai Liang Hu
Keyword(s):  
Decision Tree ◽  
Set Theory ◽  
Rough Set ◽  
Time Complexity ◽  
Mathematical Knowledge ◽  
Rough Set Theory ◽  
Effective Algorithm ◽  
Discernibility Matrix ◽  
The Core ◽  
Tree Production

Rough set theory is a popular mathematical knowledge to resolve problems which are vagueness and uncertainly. And it has been used of solving the redundancy of attribute and data. Decision tree has been widely used in data mining techniques, because it is efficient, fast and easy to be understood in terms of data classification. There are many approaches have been used to generate a decision tree. In this paper, a novel and effective algorithm is introduced for decision tree. This algorithm is based on the core of discernibility matrix on rough set theory and the degree of consistent dependence. This algorithm is to improve the decision tree on node selection. This approach reduces the time complexity of the decision tree production and space complexity compared with ID3.In the end of the article, there is an example of the algorithm can exhibit superiority.

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