scholarly journals A Decision Tree Algorithm Based on Rough Set Theory after Dimensionality Reduction

2011 ◽  
Vol 17 (7) ◽  
pp. 29-34
Author(s):  
Shailendra K. Shrivastava ◽  
Manisha Tantuway
Keyword(s):  
Decision Tree ◽  
Dimensionality Reduction ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

ROUGH SET-BASED DECISION TREE USING A CORE ATTRIBUTE

2008 ◽  
Vol 07 (02) ◽  
pp. 275-290 ◽  
Author(s):  
SANG-WOOK HAN ◽  
JAE-YEARN KIM
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Tree ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classification Rules ◽  
Decision Tree Algorithm ◽  
Decision Tree Classification ◽  
Id3 Algorithm

Decision trees are widely used in machine learning and artificial intelligence. In this paper, we extend previous research and present a new decision tree classification algorithm that uses a rough set theory to produce classification rules. Our algorithm is based on core attributes and on comparing the values of attributes between objects. Our experiments compared the performance of the Iterative Dichotomiser 3 (ID3) algorithm, C4.5, and the proposed decision tree algorithm to demonstrate its accuracy and ability to simplify rules.

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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