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Recognising and reacting to change in non-stationary data-streams is achallenging task. The majority of research in this area assumes that the trueclass label of incoming points are available, either at each time step orintermittently with some latency. In the worse case this latency approachesinfinity and we can assume that no labels are available beyond the initialtraining set. When change is expected and no further training labels areprovided the challenge of maintaining a high classification accuracy is verygreat. The challenge is to propagate the original training information throughseveral timesteps, possibly indefinitely, while adapting to underlying changein the data-stream. In this paper we conduct an initial study into theeffectiveness of using an evolving, population-based approach as the mechanismfor adapting to change. An ensemble of one-class-classifiers is maintained foreach class. Each classifier is considered as an agent in the sub-population andis subject to selection pressure to find interesting areas of the featurespace. This selection pressure forces the ensemble to adapt to the underlyingchange in the data-stream.

An Evolving Population Approach to Data-Stream Classification with Extreme Verification Latency