In a streaming environment, there is often a need for statistical prediction models to detect and adapt to concept drifts (i.e., changes in the joint distribution between predictor and response variables) so as to mitigate deteriorating predictive performance over time. Various concept drift detection approaches have been proposed in the past decades. However, they do not perform well across different concept drift types (e.g., gradual or abrupt, recurrent or irregular) and different data stream distributions (e.g., balanced and imbalanced labels). This paper presents a novel framework that can detect and also adapt to the various concept drift types, even in the presence of imbalanced data labels. The framework leverages a hierarchical set of hypothesis tests in an online fashion to detect concept drifts and employs an adaptive training strategy to significantly boost its adaptation capability. The performance of the proposed framework is compared to benchmark approaches using both simulated and real-world datasets spanning the breadth of concept drift types. The proposed approach significantly outperforms benchmark solutions in terms of precision, delay of detection as well as the adaptability across different concepts.

Common statistical prediction models often require and assume stationarity in the data. However, in many practical applications, changes in the relationship of the response and predictor variables are regularly observed over time, resulting in the deterioration of the predictive performance of these models. This paper presents Linear Four Rates (LFR), a framework for detecting these concept drifts and subsequently identifying the data points that belong to the new concept (for relearning the model). Unlike conventional concept drift detection approaches, LFR can be applied to both batch and stream data; is not limited by the distribution properties of the response variable (e.g., datasets with imbalanced labels); is independent of the underlying statistical-model; and uses user-specified parameters that are intuitively comprehensible. The performance of LFR is compared to benchmark approaches using both simulated and commonly used public datasets that span the gamut of concept drift types. The results show LFR significantly outperforms benchmark approaches in terms of recall, accuracy and delay in detection of concept drifts across datasets.