Beyond accuracy: Learning selective Bayesian classifiers with minimal test cost

• Cost-sensitive learning is often desirable in many real-world applications.
• We review the related work on naive Bayes and test-cost sensitive learning.
• We propose a new test-cost sensitive naive Bayes algorithm.
• The proposed algorithm selects an optimal attribute subset with minimal test cost.
• Experimental results on a large number of datasets validate its effectiveness.

Some existing test-cost sensitive learning algorithms are about balancing act of the misclassification cost and the total test cost, and the others focus on the balance between the classification accuracy and the total test cost. By far, however, few works reduce the total test cost, yet at the same time maintain the high classification accuracy. In order to achieve this goal, this paper modifies the backward greedy search strategy employed in selective Bayesian classifiers (SBC), which is a state-of-the-art improved naive Bayes algorithm pursuing the high classification accuracy but ignoring the total test cost. We call the resulting model test-cost sensitive naive Bayes (TCSNB). TCSNB conducts a modified backward greedy search strategy to select an optimal attribute subset with the minimal total test cost, yet at the same time maintains the high classification accuracy that characterizes SBC. Extensive empirical study validates its effectiveness and efficiency.