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Open AccessArticle

Efficient Heuristics for Structure Learning of k-Dependence Bayesian Classifier

by Yang Liu 1,2, Limin Wang 1,2 and Minghui Sun 1,2,*
1
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
2
College of Computer Science and Technology, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(12), 897; https://doi.org/10.3390/e20120897
Received: 18 October 2018 / Revised: 13 November 2018 / Accepted: 20 November 2018 / Published: 22 November 2018
(This article belongs to the Special Issue Bayesian Inference and Information Theory)
The rapid growth in data makes the quest for highly scalable learners a popular one. To achieve the trade-off between structure complexity and classification accuracy, the k-dependence Bayesian classifier (KDB) allows to represent different number of interdependencies for different data sizes. In this paper, we proposed two methods to improve the classification performance of KDB. Firstly, we use the minimal-redundancy-maximal-relevance analysis, which sorts the predictive features to identify redundant ones. Then, we propose an improved discriminative model selection to select an optimal sub-model by removing redundant features and arcs in the Bayesian network. Experimental results on 40 UCI datasets demonstrate that these two techniques are complementary and the proposed algorithm achieves competitive classification performance, and less classification time than other state-of-the-art Bayesian network classifiers like tree-augmented naive Bayes and averaged one-dependence estimators. View Full-Text
Keywords: k-dependence Bayesian classifier; minimal-redundancy-maximal-relevance analysis; discriminative model selection k-dependence Bayesian classifier; minimal-redundancy-maximal-relevance analysis; discriminative model selection
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Liu, Y.; Wang, L.; Sun, M. Efficient Heuristics for Structure Learning of k-Dependence Bayesian Classifier. Entropy 2018, 20, 897.

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