Title: Decision diagrams in machine learning: an empirical study on real-life credit-risk data
Authors: Mues, Christophe ×
Baesens, Bart
Files, C.
Vanthienen, Jan #
Issue Date: 2004
Publisher: Pergamon
Series Title: Expert Systems with Applications vol:27 issue:2 (Sept.) pages:257-264
Abstract: Decision trees are a widely used knowledge representation in machine learning. However, one of their main drawbacks is the inherent replication of isomorphic subtrees, as a result of which the produced classifiers might become too large to be comprehensible by the human experts that have to validate them. Alternatively, decision diagrams, a generalization of decision trees taking on the form of a rooted, acyclic digraph instead of a tree, have occasionally been suggested as a potentially more compact representation. Their application in machine learning has nonetheless been criticized, because the theoretical size advantages of subgraph sharing did not always directly materialize in the relatively scarce reported experiments on real-world data. Therefore, in this paper, starting from a series of rule sets extracted from three real-life credit-scoring data sets, we will empirically assess to what extent decision diagrams are able to provide a compact visual description. Furthermore, we will investigate the practical impact of finding a good attribute ordering on the achieved size savings.
ISSN: 0957-4174
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
decisiondiagrams.pdf Published 260KbAdobe PDFView/Open Request a copy

These files are only available to some KU Leuven Association staff members


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

© Web of science