The lumberjack algorithm for learning linked decision forests

Authors:
William T. B. Uther;Manuela M. Veloso
Affiliations:
Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA;Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Venue:
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Year:
2000

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Abstract

While the decision tree is an effective representation that has been used in many domains, a tree can often encode a concept inefficiently. This happens when the tree has to represent a subconcept multiple times in different parts of the tree. In this paper we introduce a new representation based on trees, the linked decision forest, that does not need to repeat internal structure. We also introduce a supervised learning algorithm. Lumberjack, that uses the new representation. We then show empirically that Lumberjack improves generalization accuracy on hierarchically decomposable concepts.