MDL-Based Decision Tree Pruning

This paper explores the application of the Minimum Description Length principle for pruning decision trees. We present a new algorithm that intuitively captures the primary goal of reducing the misclassi cation 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.

• Type: Conference
• Year: 1995
• Where: KDD
• Authors: Manish Mehta 0002, Jorma Rissanen, Rakesh Agrawal
• Keywords: Data Mining, KDD 1995, MDL Pruning Algorithm, Misclassi Cation Error, Pruning Algorithm