Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning

In many real-world applications, it is often the case that the class distribution of instances is imbalanced and the costs of misclassification are different. Thus, class-imbalance and cost-sensitive learning have attracted much attention from researchers. Sampling is one of the widely used approaches in dealing with the class imbalance problem, which alters the class distribution of instances so that the minority class is well represented in the training data. In this paper, we study the effect of sampling the natural training data on state-of-the-art Bayesian network classifiers, such as Naive Bayes (NB), Tree Augmented Naïve Bayes (TAN), Averaged One-Dependence Estimators (AODE), Weighted Average of One-Dependence Estimators (WAODE), and Hidden naive Bayes (HNB) and propose sampled Bayesian network classifiers. Our experimental results on a large number of UCI datasets show that our sampled Bayesian network classifiers perform much better than the ones trained from the natural training data especially when the natural training data is highly imbalanced and the cost ratio is high enough.