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Random Forest Based Approach for Concept Drift Handling

About research

Algorithms for concept drift handling are important for many applications. In this paper we present decision tree ensemble classification method based on the Random Forest algorithm for concept drift.
The weighted majority voting ensemble aggregation rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method. Base learner weight in our case is computed for each sample evaluation using base learners accuracy and intrinsic proximity measure of Random Forest. Our algorithm exploits both temporal weighting of samples and ensemble pruning as a forgetting strategy.
We present results of empirical comparison of our method with the state of the art classifiers for concept drift handling such as SEA, Hoeffding Adaptive tree and Online Bagging.

Paper link

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Authors

Aleksei V. Zhukov
Denis N. Sidorov
Aoife M. Foley
Adele H. Marshall

Contact

For any information related to this research contact Zhukov Aleksei