Improving learning accuracy of fuzzy decision trees by hybrid neural networks

In the process of learning from examples with fuzzy representation, the higher learning accuracy is always expected. The paper proposes using a hybrid neural network to improve the learning accuracy of the fuzzy ID3 algorithm which is a popular and powerful method of fuzzy rule extraction without much computational effort. The proposed hybrid neural network corresponds to a fuzzy reasoning method in which the concept of local weights and global weights is employed. The time to consult with domain experts to adjust the weights for improving the learning accuracy will be greatly reduced due to the learning capability of the hybrid neural network. The synergy between fuzzy decision tree induction and a hybrid neural network offers new insight into the construction of hybrid intelligent systems