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Random forest based approach for concept drift handling

Zhukov A.V., Sidorov D.N., Foley A.M. Random forest based approach for concept drift handling // Communications in Computer and Information Science. Vol.661. 2017. P.69-77. ISBN (print): 978-3-319-52920-2; 978-3-319-52919-6. DOI: 10.1007/978-3-319-52920-2_7 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...

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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