Concept drift detection methods based on different weighting strategies

Abstract

The distribution of data often evolves over time, necessitating classifiers to adjust in order to maintain optimal classification accuracy. This phenomenon, termed “concept drift”, poses a significant challenge. Detectors specifically designed for identifying concept drift are typically integrated to bolster classifier performance. In this study, we introduce two innovative methodologies designed to address the prevalent issues of high miss detection, excessive false alarms, and prolonged detection latencies encountered in many contemporary concept drift detection algorithms. The first approach, termed the Hybrid Weighting-based Concept Drift Detection Method (HW_DDM), incorporates both linear and exponential weighting for long and short windows, respectively, within a composite window model. Subsequently, concept drift is detected by calculating the weighted mean value within the window, leveraging the Hoeffding and McDirmid inequality thresholds. The second strategy, named the Dynamic Weighting-based Hoeffding Drift Detection Method (DW_HDDM), employs a mechanism that dynamically adjusts the Hoeffding threshold and dynamically weights classification prediction outcomes, thereby catering to the drift and augmenting detection efficacy. Comparative evaluations using both synthetic and real-world datasets against leading-edge algorithms are presented. The empirical results underscore that HW_DDM exhibits the lowest false detection rate with negligible miss detections on synthetic datasets. In contrast, DW_HDDM shines with minimal detection delay, reduced miss detection rates, and a diminished false detection rate, demonstrating superior classification accuracy on real-world datasets when pitted against benchmark algorithms.