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.
|Number of pages||9|
|Publication status||Published - 2017|
|Event||5th International Conference on the Analysis of Images, Social Networks and Texts - Yekaterinburg, Russian Federation|
Duration: 07 Apr 2016 → 09 Apr 2016
|Conference||5th International Conference on the Analysis of Images, Social Networks and Texts|
|Period||07/04/2016 → 09/04/2016|
- Machine learning, Decision tree, Concept drift, Ensemble learning, Classification, Random forest