Cost-sensitive decision tree and cost-sensitive naive Bayes are both new cost-sensitive learning models proposed recently to minimize the total cost of test and misclassifications. Each of them has its advantages and disadvantages. In this paper, we propose a novel cost-sensitive learning model, a hybrid cost-sensitive decision tree, called DTNB, to reduce the minimum total cost, which integrates the advantages of cost-sensitive decision tree and of the cost-sensitive naive Bayes together. We empirically evaluate it over various test strategies, and our experiments show that our DTNB outperforms cost-sensitive decision and the cost-sensitive naive Bayes significantly in minimizing the total cost of tests and misclassification based on the same sequential test strategies, and single batch strategies.
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