An Algorithm for Decision Tree Construction Based on Rough Set Theory

2008 ◽  
Author(s):  
Cuiru Wang ◽  
Fangfang Ou
Keyword(s):  
Decision Tree ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Tree Construction

Novel Approach to Decision-Tree Construction

2004 ◽  
Vol 8 (3) ◽  
pp. 332-335 ◽  
Author(s):  
Wei Jin-Mao ◽  
◽  
Wang Shu-Qin ◽  
Wang Ming-Yang
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
New Approach ◽  
Novel Approach ◽  
Tree Construction ◽  
Statistical Experiments

A new approach is presented, in which rough set theory is applied to select attributes as nodes of a decision tree. Initially, dataset is partitioned into subsets based on different condition attributes, then an attribute is chosen as a node for branching when the size of its corresponding implicit region is smaller than that of all other attributes. This approach is compared to the entropy-based method on some datasets from the UCI Machine Learning Database Repository, which instantiates the performance of the rough set approach. Statistical experiments showed that the proposed approach is feasible for decision-tree construction.

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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