A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
A methodology for automated fuzzy model generation
Fuzzy Sets and Systems
Fuzzifying Gini Index based decision trees
Expert Systems with Applications: An International Journal
Fuzzy qualitative trigonometry
International Journal of Approximate Reasoning
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Elgasir: an algorithm for creating fuzzy regression trees
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Support vector machine tree based on feature selection
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
A hierarchical approach to multi-class fuzzy classifiers
Expert Systems with Applications: An International Journal
An improved algorithm for calculating fuzzy attribute reducts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Although the induction of fuzzy decision tree (FDT) has been a very popular learning methodology due to its advantage of comprehensibility, it is often criticized to result in poor learning accuracy. Thus, one fundamental problem is how to improve the learning accuracy while the comprehensibility is kept. This paper focuses on this problem and proposes using a hybrid neural network (HNN) to refine the FDT. This HNN, designed according to the generated FDT and trained by an algorithm derived in this paper, results in a FDT with parameters, called weighted FDT. The weighted FDT is equivalent to a set of fuzzy production rules with local weights (LW) and global weights (GW) introduced in our previous work (1998). Moreover, the weighted FDT, in which the reasoning mechanism incorporates the trained LW and GW, significantly improves the FDTs' learning accuracy while keeping the FDT comprehensibility. The improvements are verified on several selected databases. Furthermore, a brief comparison of our method with two benchmark learning algorithms, namely, fuzzy ID3 and traditional backpropagation, is made. The synergy between FDT induction and HNN training offers new insight into the construction of hybrid intelligent systems with higher learning accuracy