AiGAS-dEVL-RC: An Adaptive Growing Neural Gas Model for Recurrently Drifting Unsupervised Data Streams

--Concept drift and extreme verification latency pose significant challenges in data stream learning, particularly when dealing with recurring concept changes in dynamic environments. This work introduces a novel method based on the Growing Neural Gas (GNG) algorithm, designed to effectively handle abrupt recurrent drifts while adapting to incrementally evolving data distributions (incremental drifts). Leveraging the self-organizing and topological adaptability of GNG, the proposed approach maintains a compact yet informative memory structure, allowing it to efficiently store and retrieve knowledge of past or recurring concepts, even under conditions of delayed or sparse stream supervision. Our experiments highlight the superiority of our approach over existing data stream learning methods designed to cope with incremental non-stationarities and verification latency, demonstrating its ability to quickly adapt to new drifts, robustly manage recurring patterns, and maintain high predictive accuracy with a minimal memory footprint. Unlike other techniques that fail to leverage recurring knowledge, our proposed approach is proven to be a robust and efficient online learning solution for unsupervised drifting data flows. Data stream learning has become increasingly relevant in a variety of real-world applications, ranging from fraud detection and stock market analysis to personalized recommendations and industrial process monitoring [1]. These systems rely on continuous real-time processing of data streams to make predictions or decisions. Unlike static datasets, data streams are often characterized by their unbounded, high-speed nature, which necessitates models that can operate incrementally, efficiently, and with minimal reliance on labeled data. Ensuring that such models remain accurate and adaptive over time is crucial for maintaining the performance of systems operating in dynamic environments [2]-[4].