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Evidential Decision Tree Based on Belief Entropy

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...

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Detalles Bibliográficos
Autores principales: Li, Mujin, Xu, Honghui, Deng, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515420/
http://dx.doi.org/10.3390/e21090897
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author Li, Mujin
Xu, Honghui
Deng, Yong
author_facet Li, Mujin
Xu, Honghui
Deng, Yong
author_sort Li, Mujin
collection PubMed
description 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.
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spelling pubmed-75154202020-11-09 Evidential Decision Tree Based on Belief Entropy Li, Mujin Xu, Honghui Deng, Yong Entropy (Basel) Article 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. MDPI 2019-09-16 /pmc/articles/PMC7515420/ http://dx.doi.org/10.3390/e21090897 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Mujin
Xu, Honghui
Deng, Yong
Evidential Decision Tree Based on Belief Entropy
title Evidential Decision Tree Based on Belief Entropy
title_full Evidential Decision Tree Based on Belief Entropy
title_fullStr Evidential Decision Tree Based on Belief Entropy
title_full_unstemmed Evidential Decision Tree Based on Belief Entropy
title_short Evidential Decision Tree Based on Belief Entropy
title_sort evidential decision tree based on belief entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515420/
http://dx.doi.org/10.3390/e21090897
work_keys_str_mv AT limujin evidentialdecisiontreebasedonbeliefentropy
AT xuhonghui evidentialdecisiontreebasedonbeliefentropy
AT dengyong evidentialdecisiontreebasedonbeliefentropy