Decision Diagrams in Machine Learning: An Empirical Study on Real-Life Credit-Risk Data

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, w...

• Type: Conference
• Year: 2004
• Where: DIAGRAMS
• Authors: Christophe Mues, Bart Baesens, Craig M. Files, Jan Vanthienen
• Keywords: Artificial Intelligence, Decision Diagrams, Decision Trees, DIAGRAMS 2004, Machine Learning