Paper Info
Title
MDL-Based Decision Tree Pruning
Abstract
This paper explores the application of the Min- imum Description Length principle for pruning decision trees. We present a new algorithm that intuitively captures the primary goal of reduc- ing the misclassification error. An experimental comparison is presented with three other prun- ing algorithms. The results show that the MDL pruning algorithm achieves good accuracy, small trees, and fast execution times.
Year
Venue
Keywords
1995
KDD
decision tree
Field
DocType
Citations 
Pruning algorithm,Killer heuristic,Computer science,Principal variation search,Minimum description length,Artificial intelligence,Pruning (decision trees),Null-move heuristic,Machine learning,Pruning
Conference
84
PageRank 
References 
Authors
28.38
11
3
Name
Order
Citations
PageRank
Manish Mehta1866312.14
Jorma Rissanen21665798.14
Rakesh Agrawal3297515959.33