Concept drift is a critical challenge in dynamic environments, where evolving data distributions can abruptly reduce predictive accuracy. Sudden drift requires reliable detection methods that minimize latency and false alarms, yet traditional detectors often depend on labeled data, delaying adaptation and limiting robustness.
This article introduces Neutrosophic Pseudo Labeling Sudden Drift Detection (N PSDD), a novel framework for unsupervised sudden drift detection based on neutrosophic theory. The method integrates neutrosophic clustering for pseudo labeling, block wise neural modeling, drift quantification via neutrosophic mean deviation, and adaptive threshold evaluation. By explicitly modeling truth, indeterminacy, and falsity, N PSDD captures uncertainty regions that conventional probabilistic measures fail to represent.
Experimental validation on synthetic and real world datasets demonstrates that N PSDD achieves competitive dtection latency (MTTD ≈ 23–35 instances), a lower false alarm rate (FAR ≤ 3.1%), a reduced missing drift rate (MDR ≤ 2.5%), and consistently higher G mean values (up to 0.91) than benchmark methods do. For example, on the Poker Hand dataset, N PSDD achieved MCC = 0.846 and accuracy ≈90%, while on electricity it reached MCC = 0.623 with FAR = 3.1%. In contrast, unsupervised baselines (KS WIN, HDD, MMD) yielded higher FAR (≈6–10%) and lower MCC (≤0.56), confirming their limitations in capturing real concept drift.
Overall, the N PSDD enhances the resilience of learning models under non stationary conditions and provides a robust solution for real time applications, including financial forecasting, fraud detection, and adaptive control systems.