  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.
