Classifying Evolving Data Streams Using Dynamic Streaming Random Forests

Authors:
Hanady Abdulsalam;David B. Skillicorn;Patrick Martin
Affiliations:
School of Computing, Queen's University, Kingston, Canada K7L 3N6;School of Computing, Queen's University, Kingston, Canada K7L 3N6;School of Computing, Queen's University, Kingston, Canada K7L 3N6
Venue:
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Year:
2008

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Abstract

We consider the problem of data-stream classification, introducing a stream-classification algorithm, Dynamic Streaming Random Forests, that is able to handle evolving data streams using an entropy-based drift-detection technique. The algorithm automatically adjusts its parameters based on the data seen so far. Experimental results show that the algorithm handles multi-class problems for which the underlying class boundaries drift, without losing accuracy.