A new pruning method for decision tree based on structural risk of leaf node

Pruning is an effective technique in improving the generalization performance of decision tree. However, most of the existing methods are time-consuming or unsuitable for small dataset. In this paper, a new pruning algorithm based on structural risk of leaf node is proposed. The structural risk is measured by the product of the accuracy and the volume (PAV) in leaf node. The comparison experiments with Cost-Complexity Pruning using cross-validation (CCP-CV) algorithm on some benchmark datasets show that PAV pruning largely reduces the time cost of CCP-CV, while the test accuracy of PAV pruning is close to that of CCP-CV.