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.

中文翻译：

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从标记和未标记数据中学习贝叶斯网络的高效启发式方法

贝叶斯网络分类器（BNC）是从数据中挖掘统计知识并在不确定条件下进行推断的强大工具。但是，大多数传统的BNC都集中在挖掘标记数据中存在的依存关系，而忽略隐藏在未标记数据中的信息，这可能导致决策边界出现偏差。为了解决这个问题，我们引入了一种基于互信息的新的基于订单的贪婪搜索启发式算法，用于在树状增强的朴素贝叶斯（TAN）中构建有效的结构，这是一种高度准确的学习器，同时保持了简单性和效率。目标学习用于动态描述每个未标记测试实例中的依赖关系。