There are many methods to prune decision trees, but the idea of cost-sensitive pruning and use of expert knowledge for decision tree pruning have received much less investigation even though additional flexibility and increased performance can be obtained from this method. In this paper, we introduce a cost-sensitive decision tree pruning algorithm called CC4.5 based on the C4.5 algorithm and illustrate how we use expert knowledge to help us set cost matrices. CC4.5 uses the same method as C4.5 to construct the original decision tree, but the pruning methods in CC4.5 are different from that in C4.5. CC4.5 includes three cost-sensitive pruning methods to deal with misclassification cost in the decision tree. Unlike other pruning algorithms, CC4.5 uses intelligent inexact classification and expert knowledge to consider both error and cost when pruning. Moreover, experiments show that CC4.5 results in improved decision trees with respect to the cost.