  Continual learning based on data stream mining deals with ubiquitous sources
of Big Data arriving at high-velocity and in real-time. Adaptive Random Forest
({\em ARF}) is a popular ensemble method used for continual learning due to its
simplicity in combining adaptive leveraging bagging with fast random Hoeffding
trees. While the default ARF size provides competitive accuracy, it is usually
over-provisioned resulting in the use of additional classifiers that only
contribute to increasing CPU and memory consumption with marginal impact in the
overall accuracy. This paper presents Elastic Swap Random Forest ({\em ESRF}),
a method for reducing the number of trees in the ARF ensemble while providing
similar accuracy. {\em ESRF} extends {\em ARF} with two orthogonal components:
1) a swap component that splits learners into two sets based on their accuracy
(only classifiers with the highest accuracy are used to make predictions); and
2) an elastic component for dynamically increasing or decreasing the number of
classifiers in the ensemble. The experimental evaluation of {\em ESRF} and
comparison with the original {\em ARF} shows how the two new components
contribute to reducing the number of classifiers up to one third while
providing almost the same accuracy, resulting in speed-ups in terms of
per-sample execution time close to 3x.
