Classifying Evolving Data Streams Using Dynamic Streaming Random Forests

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.

• Type: Conference
• Year: 2008
• Where: DEXA
• Authors: Hanady Abdulsalam, David B. Skillicorn, Patrick Martin
• Keywords: Database, DEXA 2008, Entropy-based Drift-detection Technique, Stream-classification Algorithm, Underlying Class Boundaries