  Decision tree is an effective classification approach in data mining and
machine learning. In applications, test costs and misclassification costs
should be considered while inducing decision trees. Recently, some
cost-sensitive learning algorithms based on ID3 such as CS-ID3, IDX,
\lambda-ID3 have been proposed to deal with the issue. These algorithms deal
with only symbolic data. In this paper, we develop a decision tree algorithm
inspired by C4.5 for numeric data. There are two major issues for our
algorithm. First, we develop the test cost weighted information gain ratio as
the heuristic information. According to this heuristic information, our
algorithm is to pick the attribute that provides more gain ratio and costs less
for each selection. Second, we design a post-pruning strategy through
considering the tradeoff between test costs and misclassification costs of the
generated decision tree. In this way, the total cost is reduced. Experimental
results indicate that (1) our algorithm is stable and effective; (2) the
post-pruning technique reduces the total cost significantly; (3) the
competition strategy is effective to obtain a cost-sensitive decision tree with
low cost.
