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Several data stream applications involve recurring concepts, i.e., concept drifts that change the underlying distribution of the data to a distribution previously seen in the data stream. Examples include electricity price prediction and tweet topic classification. In such scenario, it is useful to maintain a pool of old models that could be recovered if their knowledge matches the recurring concept well. A few existing online learning approaches maintain such pools. However, there has been a little investigation on what is the best strategy to maintain an online learning pool with a limited size. We propose to make use of diversity to decide which models to keep in the pool once the pool reaches the maximum size. The motivation behind is that a diverse pool is more likely to maintain a set of representative models with considerably different concepts, helping to handle recurring concepts. We perform experiments to investigate …