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Description
  • Data streams are usually characterized by changes in the underlying distribution generating data. Therefore algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. Rules are one of the most interpretable and flexible models for data mining prediction tasks. In this paper we present the Adaptive Very Fast Decision Rules (AVFDR), an on-line, any-time and one-pass algorithm for learning decision rules in the context of time changing data. AVFDR can learn ordered and unordered rule sets. It is able to adapt the decision model via incremental induction and specialization of rules. Detecting local drifts takes advantage of the modularity of rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules that detect drift.
  • Data streams are usually characterized by changes in the underlying distribution generating data. Therefore algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. Rules are one of the most interpretable and flexible models for data mining prediction tasks. In this paper we present the Adaptive Very Fast Decision Rules (AVFDR), an on-line, any-time and one-pass algorithm for learning decision rules in the context of time changing data. AVFDR can learn ordered and unordered rule sets. It is able to adapt the decision model via incremental induction and specialization of rules. Detecting local drifts takes advantage of the modularity of rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules that detect drift. (en)
Title
  • Handling Time Changing Data with Adaptive Very Fast Decision Rules
  • Handling Time Changing Data with Adaptive Very Fast Decision Rules (en)
skos:prefLabel
  • Handling Time Changing Data with Adaptive Very Fast Decision Rules
  • Handling Time Changing Data with Adaptive Very Fast Decision Rules (en)
skos:notation
  • RIV/00216224:14330/12:00061019!RIV13-MSM-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(LA09016), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 138610
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/12:00061019
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Data Streams; Decision Rules; Concept Drift (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [DEB58EF2C453]
http://linked.open...v/mistoKonaniAkce
  • Bristol
http://linked.open...i/riv/mistoVydani
  • Berlin / Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Machine Learning and Knowledge Discovery in Databases ECML/PKDD
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kosina, Petr
  • Gama, Joao
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-33460-3_58
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag. (Berlin; Heidelberg)
https://schema.org/isbn
  • 9783642334597
http://localhost/t...ganizacniJednotka
  • 14330
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