Holmes, G., Pfahringer, B. & Kirkby, R. (2003). Mining data streams using option trees. (Working paper 08/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Permanent Research Commons link: http://hdl.handle.net/10289/1004
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 classification 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.
University of Waikato, Department of Computer Science