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dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorKirkby, Richard Brendon
dc.coverage.spatialConference held at Osaka, Japanen_NZ
dc.identifier.citationPfahringer, B., Holmes, G. & Kirkby, R. (2008). Handling numeric attributes in Hoeffding trees. In Proceedings of 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 (pp. 296-307). Berlin: Springer.en
dc.description.abstractFor conventional machine learning classification algorithms handling numeric attributes is relatively straightforward. Unsupervised and supervised solutions exist that either segment the data into pre-defined bins or sort the data and search for the best split points. Unfortunately, none of these solutions carry over particularly well to a data stream environment. Solutions for data streams have been proposed by several authors but as yet none have been compared empirically. In this paper we investigate a range of methods for multi-class tree-based classification where the handling of numeric attributes takes place as the tree is constructed. To this end, we extend an existing approximation approach, based on simple Gaussian approximation. We then compare this method with four approaches from the literature arriving at eight final algorithm configurations for testing. The solutions cover a range of options from perfectly accurate and memory intensive to highly approximate. All methods are tested using the Hoeffding tree classification algorithm. Surprisingly, the experimental comparison shows that the most approximate methods produce the most accurate trees by allowing for faster tree growth.en
dc.publisherSpringer, Berlinen
dc.source12th Pacific-Asia Conference on Knowledge Discovery and Data Miningen_NZ
dc.subjectcomputer scienceen
dc.subjectHoeffding treeen
dc.subjectnumeric attributesen
dc.subjectMachine learning
dc.titleHandling numeric attributes in Hoeffding treesen
dc.typeConference Contributionen
dc.relation.isPartOfProc Twelfth Pacific-Asia Conference: Advances in Knowledge Discovery and Data Mining. (PAKDD 2008)en_NZ
pubs.volumeLecture Notes in Artificial Intelligence 5012en_NZ

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