Modification of random forest based approach for streaming data with concept drift

In this paper concept drift classification method was presented. Concept drift methods have potential in complex systems analysis and other processes which have stochastic nature like wind power. We present decision tree ensemble classification method based on the Random Forest algorithm for concept drift. Inspired by Accuracy Weighted Ensemble (AWE) method the weighted majority voting ensemble aggregation rule is employed. 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.