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Most predictive models assume that training and test data are generated froma stationary process. However, this assumption does not hold true in practice.In this paper, we consider the scenario of a gradual concept drift due to theunderlying non-stationarity of the data source. While previous work hasinvestigated this scenario under a supervised-learning and adaption conditions,few have addressed the common, real-world scenario when labels are onlyavailable during training. We propose a novel, iterative algorithm forunsupervised adaptation of predictive models. We show that the performance ofour batch adapted prediction algorithm is better than that of its correspondingunadapted version. The proposed algorithm provides similar (or better, in mostcases) performance within significantly less run time compared to other stateof the art methods. We validate our claims though extensive numericalevaluations on both synthetic and real data.

Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift