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  • This work address data stream mining from dynamic environments where the distribution underlying the observations may change over time. In these contexts, learning algorithms must be equipped with change detection mechanisms. Several methods have been proposed able to detect and react to concept drift. When a drift is signaled, most of the approaches use a forgetting mechanism, by releasing the current model, and start learning a new decision model. It is not rare for the concepts from history to reappear, for example seasonal changes. In this work we present method that memorizes learnt models and uses meta-learning techniques that characterize the domain of applicability of previous models. The meta-learner can detect re-occurrence of contexts and take pro-active actions by activating previous models. The main benefit of this approach is that proposed meta-learner is capable of selecting similar historical concept, if there is one, without the knowledge of true classes of examples.
  • This work address data stream mining from dynamic environments where the distribution underlying the observations may change over time. In these contexts, learning algorithms must be equipped with change detection mechanisms. Several methods have been proposed able to detect and react to concept drift. When a drift is signaled, most of the approaches use a forgetting mechanism, by releasing the current model, and start learning a new decision model. It is not rare for the concepts from history to reappear, for example seasonal changes. In this work we present method that memorizes learnt models and uses meta-learning techniques that characterize the domain of applicability of previous models. The meta-learner can detect re-occurrence of contexts and take pro-active actions by activating previous models. The main benefit of this approach is that proposed meta-learner is capable of selecting similar historical concept, if there is one, without the knowledge of true classes of examples. (en)
Title
  • Tracking Recurring Concepts with Meta-learners
  • Tracking Recurring Concepts with Meta-learners (en)
skos:prefLabel
  • Tracking Recurring Concepts with Meta-learners
  • Tracking Recurring Concepts with Meta-learners (en)
skos:notation
  • RIV/00216224:14330/09:00067155!RIV14-MSM-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • 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
  • 346625
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/09:00067155
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Data Streams; Concept Drift; Recurring Concepts (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [CF91584F0842]
http://linked.open...v/mistoKonaniAkce
  • Aveiro
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • Progress in Artificial Intelligence, Portuguese Conference on Artificial Intelligence EPIA
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kosina, Petr
  • Gama, João
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000273296300035
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-04686-5_35
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag. (Berlin; Heidelberg)
https://schema.org/isbn
  • 9783642046858
http://localhost/t...ganizacniJednotka
  • 14330
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