Bayesian network classifiers (BNCs) are powerful tools in knowledge representation and inference under conditions of uncertainty. In contrast to eager learning, lazy learning seeks to improve the classification accuracy of BNCs by forming a decision theory that is especially tailored for the testing instance, whereas it has received less attention due to the high computational cost at classification time. This study introduces the conditionally independently and identically distributed (c.i.i.d.) assumption to BNCs by assuming that all instances of the same class are conditionally independent of each other and stem from the same probability distribution. Based on this premise, we propose a novel lazy BNC, semi-lazy Bayesian network classifier (SLB), which transforms each unlabeled testing instance to a series of complete instances with discriminative supposed class labels, and then builds class-specific local BNCs for each of them. Our experimental comparison on 25 UCI datasets shows that SLB has modest training time overheads and less classification time overheads. The Friedman and Nemenyi tests show that SLB has significant zero–one loss and bias advantages over some state-of-the-art lazy learning BNCs, such as selective $k$-dependence Bayesian classifier, $k$-nearest neighbor, lazy Bayesian rule and average $n$-dependence estimators with lazy subsumption resolution.

贝叶斯网络分类器（BNC）是不确定条件下知识表示和推理的强大工具。与渴望学习相反，懒惰学习通过形成特别适合于测试实例的决策理论来寻求提高BNC的分类准确性的方法，但是由于分类时的高计算成本，它受到的关注较少。这项研究通过假设同一类别的所有实例都在条件上彼此独立并且源于相同的概率分布，向BNC引入了条件独立且均等分布（ciid）的假设。在此前提下，我们提出了一种新颖的惰性BNC，半惰性贝叶斯网络分类器（SLB），它将每个未标记的测试实例转换为带有判别性假定类标签的一系列完整实例，然后为它们中的每个构建特定于类的本地BNC。我们对25个UCI数据集的实验比较表明，SLB具有适度的训练时间开销和较少的分类时间开销。Friedman和Nemenyi测试表明，SLB与某些最新的懒惰学习BNC（例如选择性BNC）相比，具有显着的零一对一损失和偏见优势。$ķ$-依赖贝叶斯分类器， $ķ$-最近邻居，懒惰贝叶斯规则和平均值 $ñ$依赖归约估计和延迟归纳分解。