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dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorKirkby, Richard Brendon
dc.coverage.spatialConference held at Gold Coast, Australiaen_NZ
dc.identifier.citationPfahringer, B., Holmes, G. & Kirkby, R. (2007). New Options for Hoeffding Trees. In M.A. Orgun & J. Thornton(Eds), Proceedings of 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007. Berlin, Germany: Springer.en
dc.description.abstractHoeffding trees are state-of-the-art for processing high-speed data streams. Their ingenuity stems from updating sufficient statistics, only addressing growth when decisions can be made that are guaranteed to be almost identical to those that would be made by conventional batch learning methods. Despite this guarantee, decisions are still subject to limited lookahead and stability issues. In this paper we explore Hoeffding Option Trees, a regular Hoeffding tree containing additional option nodes that allow several tests to be applied, leading to multiple Hoeffding trees as separate paths. We show how to control tree growth in order to generate a mixture of paths, and empirically determine a reasonable number of paths. We then empirically evaluate a spectrum of Hoeffding tree variations: single trees, option trees and bagged trees. Finally, we investigate pruning. We show that on some datasets a pruned option tree can be smaller and more accurate than a single tree.en
dc.sourceAI 2007en_NZ
dc.subjectcomputer scienceen
dc.subjectHoeffding treeen
dc.subjectMachine learning
dc.titleNew Options for Hoeffding Treesen
dc.typeConference Contributionen
dc.relation.isPartOfProc 20th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligenceen_NZ
pubs.volumeLNCS 4830en_NZ

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