Cost-Sensitive Bayesian Network Classifiers and Their Applications in Rock Burst Prediction

Bayesian learning provides a simple but efficient method for classification by combining the sample information with the prior knowledge and the dependencies with probability estimates. However, Bayesian network classifiers that minimize the number of misclassification errors ignore different misclassification costs. For example, in rock burst prediction, the cost of misclassifying a rock which happens to burst as a rock which doesn't burst is much higher than the opposite type of error. This paper studies the cost-sensitive learning and then applies it to different Bayesian Network classifiers, and the resulted algorithms are called cost-sensitive Bayesian Network classifiers. The experimental results on 36 UCI datasets validate their effectiveness in terms of the total misclassification costs. Finally, we apply the cost-sensitive Bayesian Network classifiers to some real-world rock burst prediction examples and achieve good results.