Bayesian network classifiers remain of great interest in recent years, among which semi-naive Bayesian classifiers which utilize superparent one-dependence estimators (SPODEs) have shown superior predictive power. Linear weighting schemes are effective and efficient ones for linearly combining SPODEs, whereas it is a challenging task for averaged one-dependence estimators (AODE) to find globally optimal and fixed weights for its SPODE members. The joint probability distribution of SPODE may not always fit different test instances to the same extent, thus a flexible rather than rigid weighting scheme would be a feasible solution for the final AODE to approximate the true joint probability distribution. Based on this promise, we propose a novel instance-based weighting filter, which can flexibly assign discriminative weights to each single SPODE for different test instances. Meanwhile, the weight considers not only the mutual dependence between the superparent and class variable, but also the conditional dependence between the superparent and non-superparent attributes. Experimental comparison on 30 publicly available datasets shows that SPODE with instance-based weighting filter outperforms state-of-the-art BNCs with and without weighting methods in terms of zero–one loss, bias and variance with minimal additional computation.

近年来，贝叶斯网络分类器仍然引起人们极大的兴趣，其中利用超亲戚一独立估计量（SPODE）的半朴素贝叶斯分类器已显示出超强的预测能力。线性加权方案是线性组合SPODE的有效方法，而对于平均单依赖估计器（AODE）为其SPODE成员找到全局最优和固定权重是一项艰巨的任务。SPODE的联合概率分布可能未必总是适合不同的测试实例，因此，对于最终的AODE逼近真实的联合概率分布，灵活而不是固定的加权方案将是可行的解决方案。基于此承诺，我们提出了一种新颖的基于实例的加权过滤器，可以针对不同的测试实例灵活地为每个SPODE分配判别权重。同时，权重不仅考虑了上级变量和类变量之间的相互依赖性，而且还考虑了上级属性和非上级属性之间的条件依赖性。在30个可公开获得的数据集上进行的实验比较表明，在具有和不具有加权方法的情况下，具有基于实例的加权过滤器的SPODE在零额外损失，偏差和方差方面都优于现有的BNC，而所需的额外计算却最少。