Machine learningMachine learning

Online Decision Tree

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.

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Sources

  1. 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. DOI: 10.1145/347090.347107
  2. Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 97–106). ACM. DOI: 10.1145/502512.502529

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Referenced by

ScholarGateOnline Decision Tree (Online Decision Tree (Incremental / Streaming Decision Tree Learning)). Retrieved 2026-06-04 from https://scholargate.app/tr/machine-learning/online-decision-tree