Learning Cost-Sensitive Bayesian Networks via Direct and Indirect Methods
What is it about?
Cost-Sensitive learning has become an increasingly important area that recognizes that real world classification problems need to take the costs of misclassification and accuracy into account. Much work has been done on cost-sensitive decision tree learning, but very little has been done on cost-sensitive Bayesian networks. Although there has been significant research on Bayesian networks there has been relatively little research on learning cost-sensitive Bayesian networks. Obtaining good Bayesian networks can be challenging and hence several algorithms have been proposed for learning their structure and parameters from data. Hence an obvious question that arises is whether it is possible to develop cost-sensitive Bayesian networks and whether they would perform better than cost-sensitive decision trees for minimizing classification cost.
Why is it important?
This paper develops two new Bayesian network learning algorithms for cost-sensitive learning: (i) an indirect approach by changing the data distribution to reflect the costs of misclassification; and (ii) a direct approach that amends an existing accuracy based algorithm for learning Bayesian networks. An empirical comparison of the new approaches is carried out with cost-sensitive decision tree learning algorithms on 33 data sets, and the results show that the new algorithms perform better in terms of misclassification cost and maintaining accuracy.