A Survey on Incremental Learning Techniques for Streaming data - Akram Asdkhan Mussa; Ali Saeed Alfoudi; Ali Hakem Asaeedi (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2026)
The rapid growth of data stream applications, such as Internet of Things (IoT) systems, smart environments, and real-time analytics, has intensified the need for learning models capable of adapting to continuously evolving data distributions. The traditional techniques of batch learning presuppose fixed data and as such they find it hard to sustain performance when concept drift occurs where the characteristics of data evolve slowly, suddenly or repeatedly with time. Incremental learning has become one of the most important solutions to this issue because it allows models to keep on updating themselves with new incoming data and retain the information previously learned and does not force them to retrain at a high cost. This survey provides an in-depth overview of incremental learning approaches that are used in the streaming data setting with concept drift. We critically review supervised, unsupervised and semi-supervised machine learning methods and deep learning and hybrid methods. The review studies the fundamental adaptation techniques, such as sliding windows, replay, prototype-based learning, methods of detecting drift, expansion of dynamic architecture, and stability-plasticity balancing. To each category we comment on the underlying mechanisms, strength, weakness, computational efficiency and their ability to adapt to various drift conditions. Overall, 38 more recent studies are critically analyzed and compared in the wide range of application areas, data sets, and indicators of assessment. Some of the challenges that are highlighted in this survey include catastrophic forgetting, scalability, interpretability, and resistance to complex and recurring concept drift. Lastly, we recognize the important research gaps and establish future plans on how to develop coherent, scalable and explainable incremental learning models of actual streaming data systems in the world.