Mining data streams using option trees (revised edition, 2004)
Paper Title: Mining data streams using option trees (revised edition, 2004)
Authored By:G. Holmes, R.B. Kirkby and B. Pfahringer
Number:03/2004
Publisher:Department of Computer Science, The University of Waikato
Publication Date:2004
Pages:1-13
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 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.