A kernel-based ensemble classifier for evolving stream of trees with double concept drifting reaction

Modern mining approaches should be able to properly deal with the increased availability of structured data. Here we focus on the problem of processing streams of trees. Specifically, we cope with classification tasks. We show that by adopting a double concept drifting reaction mechanism in the context of a kernel-based ensemble of classifiers, it is actually possible to have an effective and efficient system to process streams of trees. The original contribution consists into the introduction of a local concept drifting mechanism, specifically designed for structured data, and used to compute the ensemble score function in such a way to focus only on reliable (sub)trees belonging to the classification models which constitute the ensemble. Experimental results seem to support the relevance and usefulness of this local component for concept drifting management.