  When concept drift is detected during classification in a data stream, a
common remedy is to retrain a framework's classifier. However, this loses
useful information if the classifier has learnt the current concept well, and
this concept will recur again in the future. Some frameworks retain and reuse
classifiers, but it can be time-consuming to select an appropriate classifier
to reuse. These frameworks rarely match the accuracy of state-of-the-art
ensemble approaches. For many data stream tasks, speed is important: fast,
accurate frameworks are needed for time-dependent applications. We propose the
Enhanced Concept Profiling Framework (ECPF), which aims to recognise recurring
concepts and reuse a classifier trained previously, enabling accurate
classification immediately following a drift. The novelty of ECPF is in how it
uses similarity of classifications on new data, between a new classifier and
existing classifiers, to quickly identify the best classifier to reuse. It
always trains both a new classifier and a reused classifier, and retains the
more accurate classifier when concept drift occurs. Finally, it creates a copy
of reused classifiers, so a classifier well-suited for a recurring concept will
not be impacted by being trained on a different concept. In our experiments,
ECPF classifies significantly more accurately than a state-of-the-art
classifier reuse framework (Diversity Pool) and a state-of-the-art ensemble
technique (Adaptive Random Forest) on synthetic datasets with recurring
concepts. It classifies real-world datasets five times faster than Diversity
Pool, and six times faster than Adaptive Random Forest and is not significantly
less accurate than either.
