Machine learningMachine learning

Online Random Forest

Online Random Forest (ORF) proširuje klasični Random Forest na postavke striminga, inkrementalno ažurirajući svako drvo kako nove opservacije pristižu, bez skladištenja ili ponovnog prolaska kroz ceo skup za obuku. Algoritmi kao što su Adaptive Random Forests (ARF) dodaju detekciju promena (drift detection) tako da se ansambl prilagođava kada se distribucija podataka menja tokom vremena.

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Izvori

  1. Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE. link
  2. Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfharinger, B., Holmes, G., & Abdessalem, T. (2017). Adaptive random forests for evolving data stream classification. Machine Learning, 106(9), 1469–1495. DOI: 10.1007/s10994-017-5642-8

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ScholarGate. (2026, June 3). Online Random Forest (Incremental Ensemble of Decision Trees). ScholarGate. https://scholargate.app/sr/machine-learning/online-random-forest

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ScholarGateOnline Random Forest (Online Random Forest (Incremental Ensemble of Decision Trees)). Preuzeto 2026-06-15 sa https://scholargate.app/sr/machine-learning/online-random-forest · Skup podataka: https://doi.org/10.5281/zenodo.20539026