Customers Mining of Logistics Industry Based on Neuro-Fuzzy Decision Tree

2007 ◽  
Author(s):  
Hongxia Jin ◽  
Xiaoye Niu ◽  
Li Zhang ◽  
Dongyan Zhang
Keyword(s):  
Decision Tree ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Logistics Industry ◽  
Neuro Fuzzy

Induction of fuzzy decision trees and its refinement using gradient projected-neuro-fuzzy decision tree

2014 ◽  
Vol 6 (4) ◽  
pp. 346 ◽  
Author(s):  
Swathi Jamjala Narayanan ◽  
Rajen B. Bhatt ◽  
Ilango Paramasivam ◽  
M. Khalid ◽  
B.K. Tripathy
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Neuro Fuzzy ◽  
Fuzzy Decision Trees

An Improved Second Order Training Algorithm for Improving the Accuracy of Fuzzy Decision Trees

2016 ◽  
Vol 5 (4) ◽  
pp. 96-120 ◽  
Author(s):  
Swathi Jamjala Narayanan ◽  
Rajen B. Bhatt ◽  
Ilango Paramasivam
Keyword(s):  
Decision Tree ◽  
Second Order ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Neuro Fuzzy ◽  
Selection Threshold ◽  
Induction Process ◽  
Fuzzy Partitions ◽  
Fuzzy Decision Trees ◽  
Interpretable Classification

Fuzzy decision tree (FDT) is a powerful top-down, hierarchical search methodology to extract human interpretable classification rules. The performance of FDT depends on initial fuzzy partitions and other parameters like alpha-cut and leaf selection threshold. These parameters are decided either heuristically or by trial-and-error. For given set of parameters, FDT is constructed using any standard induction algorithms like Fuzzy ID3. Due to the greedy nature of induction process, there is a chance of FDT resulting in poor classification accuracy. To further improve the accuracy of FDT, in this paper, the authors propose the strategy called Improved Second Order- Neuro- Fuzzy Decision Tree (ISO-N-FDT). ISO-N-FDT tunes parameters of FDT from leaf node to roof node starting from left side of tree to its right and attains better improvement in accuracy with less number of iterations exhibiting fast convergence and powerful search ability.

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