Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 174-179.DOI: 10.3778/j.issn.1002-8331.2010-0045

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Improved Algorithm of Fuzzy Decision Tree Based on Neural Network

ZHANG Min, PENG Hongwei, YAN Xiaoling   

  1. College of Information Engineering, Dalian University, Dalian, Liaoning 116622, China
  • Online:2021-11-01 Published:2021-11-04



  1. 大连大学 信息工程学院,辽宁 大连 116622


Traditional decision tree finds the decision boundary by recursively dividing the feature space. It gives a “hard” division of the feature space and accurately describes of data. However, when dealing with big data and complex model problems, this precise decision boundary reduces generalization ability of decision tree. In order decision tree automatically acquire inaccurate knowledge, fuzzy theory is introduced into decision tree and in the process of building, neural network is introduced as decision leaf node. An improved algorithm of fuzzy decision tree based on neural network is proposed. In neural network fuzzy decision tree, classifier learning consists two stage:the first stage uses a heuristic algorithm with reduced uncertainty to divide the big data, and stops the growth of fuzzy decision tree until the node dividing ability is below reality threshold. The second stage uses neural network to classify the leaf node with generalization ability. Experiments show that compared with traditional classification learning algorithm, proposed algorithm has higher accuracy, and can determine the size of decision tree through structural adaptation for classification problem of big data and complex patterns.

Key words: decision tree, neural network, fuzzy theory, fuzzy decision tree based on neural network



关键词: 决策树, 神经网络, 模糊理论, 神经网络模糊决策树