A sample selection algorithm in fuzzy decision tree induction and its theoretical analyses

2007 ◽  
Author(s):  
Xi-Zhao Wang ◽  
Jian-Hui Yan ◽  
Ran Wang ◽  
Chun-Ru Dong
Keyword(s):  
Decision Tree ◽  
Sample Selection ◽  
Fuzzy Decision ◽  
Selection Algorithm ◽  
Fuzzy Decision Tree ◽  
Decision Tree Induction ◽  
Theoretical Analyses

Dynamic programming based fuzzy partition in fuzzy decision tree induction

2020 ◽  
Vol 39 (5) ◽  
pp. 6757-6772
Author(s):  
Yashuang Mu ◽  
Lidong Wang ◽  
Xiaodong Liu
Keyword(s):  
Dynamic Programming ◽  
Decision Tree ◽  
Decision Trees ◽  
Dynamic Programming Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Fuzzy Partition ◽  
Decision Tree Induction ◽  
Fuzzy Partitions ◽  
Fuzzy Decision Trees

Fuzzy decision trees are one of the most popular extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Among the majority of fuzzy decision trees learning methods, the number of fuzzy partitions is given in advance, that is, there are the same amount of fuzzy items utilized in each condition attribute. In this study, a dynamic programming-based partition criterion for fuzzy items is designed in the framework of fuzzy decision tree induction. The proposed criterion applies an improved dynamic programming algorithm used in scheduling problems to establish an optimal number of fuzzy items for each condition attribute. Then, based on these fuzzy partitions, a fuzzy decision tree is constructed in a top-down recursive way. A comparative analysis using several traditional decision trees verify the feasibility of the proposed dynamic programming based fuzzy partition criterion. Furthermore, under the same framework of fuzzy decision trees, the proposed fuzzy partition solution can obtain a higher classification accuracy than some cases with the same amount of fuzzy items.

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