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Description
  • This work addresses the problem of mining data streams generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnose degradations of this process, using change detection mechanisms, and self-repair the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learner can detect recurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models. The experimental evaluation on three text mining problems demonstrates the main advantages of the proposed system: it provides information about the recurrence of concepts and rapidly adapts decision models when drift occurs.
  • This work addresses the problem of mining data streams generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnose degradations of this process, using change detection mechanisms, and self-repair the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learner can detect recurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models. The experimental evaluation on three text mining problems demonstrates the main advantages of the proposed system: it provides information about the recurrence of concepts and rapidly adapts decision models when drift occurs. (en)
Title
  • Recurrent concepts in data streams classification
  • Recurrent concepts in data streams classification (en)
skos:prefLabel
  • Recurrent concepts in data streams classification
  • Recurrent concepts in data streams classification (en)
skos:notation
  • RIV/00216224:14330/13:00068485!RIV14-MSM-14330___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(LG13010)
http://linked.open...iv/cisloPeriodika
  • May
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
  • 101796
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/13:00068485
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Data streams; Concept drift; Meta-learning; Recurrent concepts (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • GB - Spojené království Velké Británie a Severního Irska
http://linked.open...ontrolniKodProRIV
  • [478A31800E1F]
http://linked.open...i/riv/nazevZdroje
  • Knowledge and Information Systems
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...v/svazekPeriodika
  • roč. 2013
http://linked.open...iv/tvurceVysledku
  • Kosina, Petr
  • Gama, João
issn
  • 0219-1377
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/s10115-013-0654-6
http://localhost/t...ganizacniJednotka
  • 14330
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