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Random forest based approach for concept drift handling

... 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 ...

Теги: machine learning , decision tree , concept drift , ensemble learning , classification , random forest , classification (of information) , decision trees , image analysis , learning algorithms , learning systems , wind power , aggregation rules , concept drifts , empiri
Раздел: ИСЭМ СО РАН


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