Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15930
Title: Improving learning accuracy of fuzzy decision trees by hybrid neural networks
Authors: Tsang, ECC
Wang, XZ
Yeung, DS
Issue Date: 2000
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on fuzzy systems, 2000, v. 8, no. 5, p. 601-614 How to cite?
Journal: IEEE transactions on fuzzy systems 
Abstract: 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
URI: http://hdl.handle.net/10397/15930
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/91.873583
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