방법 비교

	온라인 K-최근접 이웃	온라인 결정 트리
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2010s (formalized in streaming-learning literature)	2000
창시자	Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016)	Domingos, P. & Hulten, G.
유형	Instance-based online classifier/regressor	Incremental supervised classifier
원전	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.
별칭	Online KNN, Incremental KNN, Streaming KNN, KNN with concept drift adaptation	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree
관련	5	6
요약	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.