id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_aid2rvqau5dnffyxvubsm7mhya	Martin Sarnovsky	Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble	2021	31	.pdf	application/pdf	16959	4240	80	model for the data streams classification, which utilizes the dynamic class weighting Keywords Ensemble learning, Concept drift, Data streams, Adaptive ensemble advanced machine learning methods that can reflect the changing concepts in data streams concepts change over time, dynamic adaptive ensembles present a suitable method that For predictive data modeling applied on the drifting streams, advanced adaptive Ensemble models represent a popular solution for the classification of drifting data of the DDCW model with the selected other streaming ensemble-based classifiers. The DDCW model proved to be suitable for data streams with different concept drifts Table 4 Comparison of accuracy and F1 metrics of evaluated ensemble models on the real data streams. Table 5 Comparison of accuracy and F1 metrics of evaluated ensemble models on the synthetic data streams. Concept drift detection for data stream learning based Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble	./cache/work_aid2rvqau5dnffyxvubsm7mhya.pdf	./txt/work_aid2rvqau5dnffyxvubsm7mhya.txt