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Open AccessArticle

Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

by 1,2, 2,* and 1,2,*
1
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
*
Authors to whom correspondence should be addressed.
Entropy 2017, 19(12), 661; https://doi.org/10.3390/e19120661
Received: 31 October 2017 / Revised: 26 November 2017 / Accepted: 30 November 2017 / Published: 3 December 2017
(This article belongs to the Special Issue Symbolic Entropy Analysis and Its Applications)
Bayesian network classifiers (BNCs) have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN) classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN) by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead. View Full-Text
Keywords: Bayesian network classifiers; label-driven learning framework; instance-based learning; ensemble learning; information theory Bayesian network classifiers; label-driven learning framework; instance-based learning; ensemble learning; information theory
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MDPI and ACS Style

Sun, Y.; Wang, L.; Sun, M. Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels. Entropy 2017, 19, 661. https://doi.org/10.3390/e19120661

AMA Style

Sun Y, Wang L, Sun M. Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels. Entropy. 2017; 19(12):661. https://doi.org/10.3390/e19120661

Chicago/Turabian Style

Sun, Yi; Wang, Limin; Sun, Minghui. 2017. "Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels" Entropy 19, no. 12: 661. https://doi.org/10.3390/e19120661

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