Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift
This addresses the incremental challenge of adapting models to non-stationary data in real-world scenarios where labels are scarce after training.
The paper tackles the problem of predictive model adaptation under gradual concept drift when labels are only available during training, proposing an unsupervised iterative algorithm that improves performance over unadapted versions and matches or exceeds state-of-the-art methods with significantly less run time.
Most predictive models assume that training and test data are generated from a 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 the underlying non-stationarity of the data source. While previous work has investigated this scenario under a supervised-learning and adaption conditions, few have addressed the common, real-world scenario when labels are only available during training. We propose a novel, iterative algorithm for unsupervised adaptation of predictive models. We show that the performance of our batch adapted prediction algorithm is better than that of its corresponding unadapted version. The proposed algorithm provides similar (or better, in most cases) performance within significantly less run time compared to other state of the art methods. We validate our claims though extensive numerical evaluations on both synthetic and real data.