MDL-Based Decision Tree Pruning

Manish Mehta, Jorma Rissanen, and Rakesh Agrawal, IBM Almaden Research Center

This paper explores the application of the Minimum Description Length principle for pruning decision trees. We present a new algorithm that more intuitively captures the primary goal of reducing the misclassification error. An experimental comparison is presented with three other pruning algorithms. The results show that the MDL pruning algorithm achieves good accuracy, small trees, and fast execution times.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.