Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift

Zhehu Yuan, Yinqi Sun, Dennis Shasha

Research output: Contribution to journalArticlepeer-review


Database and data structure research can improve machine learning performance in many ways. One way is to design better algorithms on data structures. This paper combines the use of incremental computation as well as sequential and probabilistic filtering to enable “forgetful” tree-based learning algorithms to cope with streaming data that suffers from concept drift. (Concept drift occurs when the functional mapping from input to classification changes over time). The forgetful algorithms described in this paper achieve high performance while maintaining high quality predictions on streaming data. Specifically, the algorithms are up to 24 times faster than state-of-the-art incremental algorithms with, at most, a 2% loss of accuracy, or are at least twice faster without any loss of accuracy. This makes such structures suitable for high volume streaming applications.

Original languageEnglish (US)
Article number278
Issue number6
StatePublished - Jun 2023


  • concept drift
  • incremental algorithms
  • machine learning
  • tree data structures

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Theory and Mathematics
  • Computational Mathematics


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