Mending is Better than Ending: Adapting Immutable Classifiers to Nonstationary Environments using Ensembles of Patches

In times of abundant streams of data, the only constant we can rely on is change itself. Given an existing classification model, it might be impractical or impossible to retrain when concept drift impairs the performance. In this paper, we propose an ensemble learning approach that fixes errors of a given model via patches: smaller, local models that are applied when the base model is likely to fail. These patches are added to the ensemble when the base learner shows decreased performance for one or multiple classes, which we discover by applying explicit drift detection. Each patch covers the drift of a subset of the classes. Via the drift detection method, the misclassification behavior of the base learner can be matched to an existing patch. This reduces the total amount of patches to the number of seen concept drifts, which is an advantage in the long run. Patches can be added to the ensemble and refined incrementally. The ensemble decision is conducted by a sequential classifier, which allows a high number of ensemble members that can be added incrementally. Experimental results show that ensemble patching performs well w.r.t. accuracy and adaptation speed.