A Classifier Learning Method Based on Tree-Augmented Naïve Bayes

Xi CHEN; Kun ZHANG

doi:10.11999/JEIT180886

Volume 41 Issue 8

Aug. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(8): 2001-2008

Xi CHEN, Kun ZHANG. A Classifier Learning Method Based on Tree-Augmented Naïve Bayes[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2001-2008. doi: 10.11999/JEIT180886

Citation:

Xi CHEN, Kun ZHANG. A Classifier Learning Method Based on Tree-Augmented Naïve Bayes[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2001-2008. doi: 10.11999/JEIT180886

Xi CHEN, Kun ZHANG. A Classifier Learning Method Based on Tree-Augmented Naïve Bayes[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2001-2008. doi: 10.11999/JEIT180886

Citation:

Xi CHEN, Kun ZHANG. A Classifier Learning Method Based on Tree-Augmented Naïve Bayes[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2001-2008. doi: 10.11999/JEIT180886

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A Classifier Learning Method Based on Tree-Augmented Naïve Bayes

doi: 10.11999/JEIT180886

Xi CHEN,
Kun ZHANG^,

1.
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
2.
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China

Funds: The National Natural Science Foundation of China (61772087)

Received Date: 2018-09-18
Rev Recd Date: 2019-03-27

Available Online: 2019-04-20

Publish Date: 2019-08-01

Abstract

Abstract

The structure of Tree-Augmented Naïve Bayes (TAN) forces each attribute node to have a class node and a attribute node as parent, which results in poor classification accuracy without considering correlation between each attribute node and the class node. In order to improve the classification accuracy of TAN, firstly, the TAN structure is proposed that allows each attribute node to have no parent or only one attribute node as parent. Then, a learning method of building the tree-like Bayesian classifier using a decomposable scoring function is proposed. Finally, the low-order Conditional Independency (CI) test is applied to eliminating the useless attribute, and then based on improved Bayesian Information Criterion (BIC) function, the classification model with acquired the parent node of each attribute node is established using the greedy algorithm. Through comprehensive experiments, the proposed classifier outperforms Naïve Bayes (NB) and TAN on multiple classification, and the results prove that this learning method has certain advantages.
- Bayesian classifier,
- Tree-Augmented Naïve Bayes (TAN),
- Scoring function

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References(19)

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