Linganisha mbinu

	Ensemble K-Nearest Neighbors	Kikundi cha Kura (Voting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2000s	1990s–2004
Mwanzilishi	Domeniconi, C. & Yan, B. (key formalization)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Aina	Ensemble (aggregated KNN classifiers/regressors)	Ensemble (combination of multiple classifiers by vote)
Chanzo asilia	Domeniconi, C., & Yan, B. (2004). Nearest neighbor ensemble. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Vol. 1, pp. 228–231. IEEE.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Majina mbadala	Ensemble KNN, KNN ensemble, aggregated k-nearest neighbors, combined KNN	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Zinazohusiana	5	5
Muhtasari	Ensemble K-Nearest Neighbors combines multiple KNN models — each trained with a different value of k, distance metric, feature subset, or data bootstrap — and aggregates their predictions by majority vote (classification) or averaging (regression). The approach reduces the high variance inherent in any single KNN model and produces more stable, accurate predictions on tabular data.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.