The Lumberjack Algorithm for Learning Linked Decision Forests

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.

• Type: Conference
• Year: 2000
• Where: PRICAI
• Authors: William T. B. Uther, Manuela M. Veloso
• Keywords: Artificial Intelligence, Decision Tree, PRICAI 2000, Subconcept Multiple Times, Tree