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Structure Learning of Bayesian Network Based on Adaptive Thresholding

1,2, 1,2, 1,2 and 1,2,*
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
Author to whom correspondence should be addressed.
Entropy 2019, 21(7), 665;
Received: 1 June 2019 / Revised: 2 July 2019 / Accepted: 5 July 2019 / Published: 8 July 2019
PDF [495 KB, uploaded 9 July 2019]


Direct dependencies and conditional dependencies in restricted Bayesian network classifiers (BNCs) are two basic kinds of dependencies. Traditional approaches, such as filter and wrapper, have proved to be beneficial to identify non-significant dependencies one by one, whereas the high computational overheads make them inefficient especially for those BNCs with high structural complexity. Study of the distributions of information-theoretic measures provides a feasible approach to identifying non-significant dependencies in batch that may help increase the structure reliability and avoid overfitting. In this paper, we investigate two extensions to the k-dependence Bayesian classifier, MI-based feature selection, and CMI-based dependence selection. These two techniques apply a novel adaptive thresholding method to filter out redundancy and can work jointly. Experimental results on 30 datasets from the UCI machine learning repository demonstrate that adaptive thresholds can help distinguish between dependencies and independencies and the proposed algorithm achieves competitive classification performance compared to several state-of-the-art BNCs in terms of 0–1 loss, root mean squared error, bias, and variance. View Full-Text
Keywords: Bayesian network classifiers; mutual information; conditional mutual information; thresholding Bayesian network classifiers; mutual information; conditional mutual information; thresholding

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Zhang, Y.; Wang, L.; Duan, Z.; Sun, M. Structure Learning of Bayesian Network Based on Adaptive Thresholding. Entropy 2019, 21, 665.

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