MATEC Web Conf.
Volume 79, 2016VII Scientific Conference with International Participation “Information-Measuring Equipment and Technologies” (IME&T 2016)
|Number of page(s)||8|
|Published online||11 October 2016|
Design of Neuro-Fuzzy Decision Trees
National Research Tomsk State University, 634050, Tomsk, Russia
* Corresponding author: firstname.lastname@example.org
In order to improve accuracy of fuzzy decision trees classification we propose a procedure of parameters adaptation by means of neural network training. In the direct cycle, fuzzy decision trees are based on the algorithm of fuzzy ID3; in the feedback cycle, parameters of fuzzy decision trees are adapted based on stochastic gradient algorithm by traverse to the root nodes back from the leaves. Using this strategy, the hierarchical structure of the fuzzy decision trees remains fixed.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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