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Mining data streams using option trees (revised edition, 2004)

Abstract
The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time. This paper describes a data stream classi_cation algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset.
Type
Working Paper
Type of thesis
Series
Computer Science Working Papers
Citation
Holmes, G., Kirkby, R.,& Pfahringer, B. (2004). Mining Data Streams Using Option Trees. Rev. ed. (Working paper series. University of Waikato, Department of Computer Science. No. 03/2004). Hamilton, New Zealand: University of Waikato.
Date
2004-01-01
Publisher
Department of Computer Science, The University of Waikato
Degree
Supervisors
Rights