Cost-sensitive Bayesian network classifiers

Abstract

Cost-sensitive learning has received increased attention in recent years. However, in existing studies, most of the works are devoted to make decision trees cost-sensitive and very few works discuss cost-sensitive Bayesian network classifiers. In this paper, an instance weighting method is incorporated into various Bayesian network classifiers. The probability estimation of Bayesian network classifiers is modified by the instance weighting method, which makes Bayesian network classifiers cost-sensitive. The experimental results on 36 UCI data sets show that when cost ratio is large, the cost-sensitive Bayesian network classifiers perform well in terms of the total misclassification costs and the number of high cost errors. When cost ratio is small, the advantage of cost-sensitive Bayesian network classifiers is not so obvious in terms of the total misclassification costs, but still obvious in terms of the number of high cost errors, compared to the original cost-insensitive Bayesian network classifiers.