“…These algorithms focus on updating the model efficiently (Lv et al, 2019;Tian et al, 2019;Zhao et al, 2021;Ding et al, 2024), handling concept drift (Schwarzerova and Bajger, 2021), managing memory constraints (Smith et al, 2021), and balancing stability and plasticity in the learned knowledge (Wu et al, 2021;Lin et al, 2022;Kim and Han, 2023). Additionally, incremental learning has been explored in different domains, including image classification (Meng et al, 2022;Nguyen et al, 2022;Zhao et al, 2022), natural language processing (Jan Moolman Buys University College University of Oxford, 2017; Kahardipraja et al, 2023), recommender systems (Ouyang et al, 2021;Wang et al, 2021;Ahrabian et al, 2021a), and data stream mining (Eisa et al, 2022). Researchers have investigated different strategies such as incremental decision trees (Barddal and Fabr'ıcio Enembreck., 2020;Choyon et al, 2020;Han et al, 2023), online clustering (Bansiwala et al, 2021), ensemble methods (Lovinger and Valova, 2020;Zhang J. et al, 2023), and deep learning approaches to tackle incremental learning problems (Ali et al, 2022).…”