Abstract
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.
Highlights
Domestic and foreign literature describes the use of decision trees as a powerful evolutionary methodology for solving problems of classification and regression [1,2,3,4,5]
In order to improve the accuracy of classification, the author suggests using neuro-fuzzy decision trees, which have property to adapt parameters by means of neural network training
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
Summary
Domestic and foreign literature describes the use of decision trees as a powerful evolutionary methodology for solving problems of classification and regression [1,2,3,4,5]. Being a DATA MINING tool (detection of hidden knowledge from data), decision trees are used for search and retrieval of interpretable classification rules, which are clear for humans. We should note that many packages for intellectual data analysis already contain methods for constructing decision trees; they are the perfect tool for decision support systems
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