Concept Drift Detection for Streaming Data
This addresses the challenge of maintaining model performance in real-time applications with non-stationary data, though it appears incremental as it builds on existing concept drift detection methods.
The paper tackles the problem of concept drift in streaming data, where predictive models degrade over time due to changes in data relationships, and presents the Linear Four Rates (LFR) framework, which significantly outperforms benchmarks in recall, accuracy, and detection delay across datasets.
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.