An effective just-in-time adaptive classifier for gradual concept drifts

Classification systems designed to work in nonstationary conditions rely on the ability to track the monitored process by detecting possible changes and adapting their knowledge-base accordingly. Adaptive classifiers present in the literature are effective in handling abrupt concept drifts (i.e., sudden variations), but, unfortunately, they are not able to adapt to gradual concept drifts (i.e., smooth variations) as these are, in the best case, detected as a sequence of abrupt concept drifts. To address this issue we introduce a novel adaptive classifier that is able to track and adapt its knowledge base to gradual concept drifts (modeled as polynomial trends in the expectations of the conditional probability density functions of input samples), while maintaining its effectiveness in dealing with abrupt ones. Experimental results show that the proposed classifier provides high classification accuracy both on synthetically generated datasets and measurements from real sensors.