Neuro-fuzzy ID3: a method of inducing fuzzy decision trees with linear programming for maximizing entropy and an algebraic method for incremental learning

1996 ◽  
Vol 81 (1) ◽  
pp. 157-167 ◽  
Author(s):  
H. Ichihashi ◽  
T. Shirai ◽  
K. Nagasaka ◽  
T. Miyoshi
Keyword(s):  
Linear Programming ◽  
Decision Trees ◽  
Incremental Learning ◽  
Algebraic Method ◽  
Fuzzy Decision ◽  
Neuro Fuzzy ◽  
Fuzzy Decision Trees

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

NEURO-FUZZY DECISION TREES

2006 ◽  
Vol 16 (01) ◽  
pp. 63-78 ◽  
Author(s):  
RAJEN B. BHATT ◽  
M. GOPAL
Keyword(s):  
Decision Trees ◽  
Adaptation Strategy ◽  
Stochastic Gradient Descent ◽  
Fuzzy Decision ◽  
Parameter Adaptation ◽  
Neuro Fuzzy ◽  
Gradient Descent Algorithm ◽  
The Hierarchical Structure ◽  
Real World Datasets ◽  
Fuzzy Decision Trees

Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets.

scholarly journals ERRATUM: "NEURO-FUZZY DECISION TREES"

2006 ◽  
Vol 16 (04) ◽  
pp. 319-319 ◽  
Author(s):  
RAJEN B. BHATT ◽  
M. GOPAL
Keyword(s):  
Decision Trees ◽  
Fuzzy Decision ◽  
Neuro Fuzzy ◽  
Fuzzy Decision Trees
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