Random Forest Based Approach for Concept Drift Handling
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Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. 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 ensemble pruning as a forgetting strategy. We present results of empirical comparison of our method and other state-of-the-art concept-drfit classifiers.
KeywordsMachine learning Decision tree Concept drift Ensemble learning Classification Random forest
This work is funded by the RSF grant No. 14-19-00054 and by the International science and technology cooperation program of China, project 2015DFR70850, NSFC Grant No. 61673398.
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