Knowledge pruning in decision trees

We propose a novel pruning method of decision trees based on domain knowledge, semantic hierarchies among classes, which is used to generate decision trees by relaxing the levels of hierarchies for both height and width of the trees. We develop the algorithm, and the effectiveness is examined by UCI Machine Learning Repository: On Car Evaluation and Nursery. We can generate the decision trees consisting of 11 and 13 rules, although C4.5 generates 182 and 572 rules, respectively.