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.
The work reported in this paper has been supported by Kuwait University.
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Abdulsalam, H., Skillicorn, D.B., Martin, P. (2008). Classifying Evolving Data Streams Using Dynamic Streaming Random Forests. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2008. Lecture Notes in Computer Science, vol 5181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85654-2_54
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DOI: https://doi.org/10.1007/978-3-540-85654-2_54
Publisher Name: Springer, Berlin, Heidelberg
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