Σύγκριση μεθόδων

	Online K-Nearest Neighbors	Δέντρο Απόφασης σε Πραγματικό Χρόνο
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2010s (formalized in streaming-learning literature)	2000
Δημιουργός	Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016)	Domingos, P. & Hulten, G.
Τύπος	Instance-based online classifier/regressor	Incremental supervised classifier
Θεμελιώδης πηγή	Losing, V., Hammer, B., & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), pp. 291–300. IEEE.	Domingos, P., & Hulten, G. (2000). Mining very fast data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 71–80). ACM.
Εναλλακτικές ονομασίες	Online KNN, Incremental KNN, Streaming KNN, KNN with concept drift adaptation	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree
Συναφείς	5	6
Σύνοψη	Online K-Nearest Neighbors (Online KNN) adapts the classic KNN algorithm to a data-stream setting where observations arrive sequentially and the model must update incrementally without full retraining. Instead of storing all historical instances, it maintains a bounded sliding window or adaptive memory, using the most recent and most representative examples to classify or predict each incoming point by proximity.	An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time classification on potentially infinite data streams.