Learning in the Presence of Concept Drift and Hidden Contexts View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1996-04

AUTHORS

Gerhard Widmer, Miroslav Kubat

ABSTRACT

On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and re-using them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' performance under various conditions such as different levels of noise and different extent and rate of concept drift. More... »

PAGES

69-101

References to SciGraph publications

  • 1991-01. Instance-based learning algorithms in MACHINE LEARNING
  • 1993. Effective learning in dynamic environments by explicit context tracking in MACHINE LEARNING: ECML-93
  • 1988-04. Queries and concept learning in MACHINE LEARNING
  • 1991-05. A nearest hyperrectangle learning method in MACHINE LEARNING
  • 1986-09. Incremental learning from noisy data in MACHINE LEARNING
  • 1993-01. A weighted nearest neighbor algorithm for learning with symbolic features in MACHINE LEARNING
  • 1986-03. Explanation-based generalization: A unifying view in MACHINE LEARNING
  • 1993-10. Flexible concept learning in real-time systems in JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • 1994-06. Associative reinforcement learning: Functions ink-DNF in MACHINE LEARNING
  • 1983. A Theory and Methodology of Inductive Learning in MACHINE LEARNING
  • 1982-10. Rough sets in INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING
  • 1987-09. Experiments with incremental concept formation: UNIMEM in MACHINE LEARNING
  • 1994-01. Tracking drifting concepts by minimizing disagreements in MACHINE LEARNING
  • 1993. COBBIT—A control procedure for COBWEB in the presence of concept drift in MACHINE LEARNING: ECML-93
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1023/a:1018046501280

    DOI

    http://dx.doi.org/10.1023/a:1018046501280

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1022457085


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