Affiliations: [a] College of Computer Science and Technology, Jilin University, Changchun, Jilin, China | [b] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
Correspondence:
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Corresponding author: Minghui Sun, College of Computer Science and Technology, Jilin University, Changchun, Jilin, China. E-mail: [email protected].
Abstract: Bayesian network classifiers (BNCs) are powerful tools to mine statistical knowledge from data and infer under conditions of uncertainty. However, most of the traditional BNCs focus on mining the dependency relationships existed in labeled data while neglecting the information hidden in unlabeled data, which may result in the biased decision boundaries. To address this issue, we introduce a new order-based greedy search heuristic based on mutual information for building efficient structures in tree-augmented naive Bayes (TAN), which is a highly accurate learner while maintaining simplicity and efficiency. Target learning is used to dynamically describe the dependency relationships in each unlabeled test instance. Extensive experimental results on UCI (University of California at Irvine) machine learning repository demonstrate that our proposed algorithm is a competitive alternative to state-of-the-art classifiers like weighted averaged TAN and k-dependence Bayesian classifier, as well as Random forest.
