Title: A comparison of pruning criteria for probability trees
Authors: Fierens, Daan ×
Ramon, Jan
Blockeel, Hendrik
Bruynooghe, Maurice #
Issue Date: Jan-2010
Publisher: Springer New York LLC
Series Title: Machine Learning vol:78 issue:1-2 pages:251-285
Abstract: Probability trees are decision trees that predict class probabilities rather than the most likely class. The pruning criterion used to learn a probability tree strongly influences the size of the tree and thereby also the quality of its probability estimates. While the effect of pruning criteria on classification accuracy is well-studied, only recently there is more interest in the effect on probability estimates. Hence, it is currently unclear which pruning criteria for probability trees are preferable under which circumstances.

In this paper we survey six of the most important pruning criteria for probability trees, and discuss their theoretical advantages and disadvantages. We also perform an extensive experimental study of the relative performance of these pruning criteria. The main conclusion is that overall a pruning criterion based on randomization tests performs best because it is most robust to extreme data characteristics (such as class skew or a high number of classes). We also identify and explain several shortcomings of the other pruning criteria.
ISSN: 0885-6125
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
mlj.pdfMain article Published 272KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science