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Entropy in Dynamic Systems
Open AccessArticle

Evidential Decision Tree Based on Belief Entropy

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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Entropy 2019, 21(9), 897; https://doi.org/10.3390/e21090897
Received: 12 July 2019 / Revised: 13 August 2019 / Accepted: 13 September 2019 / Published: 16 September 2019
Decision Tree is widely applied in many areas, such as classification and recognition. Traditional information entropy and Pearson’s correlation coefficient are often applied as measures of splitting rules to find the best splitting attribute. However, these methods can not handle uncertainty, since the relation between attributes and the degree of disorder of attributes can not be measured by them. Motivated by the idea of Deng Entropy, it can measure the uncertain degree of Basic Belief Assignment (BBA) in terms of uncertain problems. In this paper, Deng entropy is used as a measure of splitting rules to construct an evidential decision tree for fuzzy dataset classification. Compared to traditional combination rules used for combination of BBAs, the evidential decision tree can be applied to classification directly, which efficiently reduces the complexity of the algorithm. In addition, the experiments are conducted on iris dataset to build an evidential decision tree that achieves the goal of more accurate classification. View Full-Text
Keywords: decision; belief entropy; Deng entropy; basic belief assignment; fuzzy data classification decision; belief entropy; Deng entropy; basic belief assignment; fuzzy data classification
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Li, M.; Xu, H.; Deng, Y. Evidential Decision Tree Based on Belief Entropy. Entropy 2019, 21, 897.

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