Random forest based approach for concept drift handling: Analysis of Images, Social Networks and Texts: 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers

Aleksei V. Zhukov, Denis N. Sidorov, Aoife M. Foley

Research output: Contribution to conferencePaperpeer-review

31 Citations (Scopus)
226 Downloads (Pure)

Abstract

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-drift classifiers.
Original languageEnglish
Pages69-77
Number of pages9
Publication statusPublished - 2017
Event5th International Conference on the Analysis of Images, Social Networks and Texts - Yekaterinburg, Russian Federation
Duration: 07 Apr 201609 Apr 2016
http://aistconf.org/

Conference

Conference5th International Conference on the Analysis of Images, Social Networks and Texts
Country/TerritoryRussian Federation
CityYekaterinburg
Period07/04/201609/04/2016
Internet address

Keywords

  • Machine learning, Decision tree, Concept drift, Ensemble learning, Classification, Random forest

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