Learning about the Learning Process

  • João Gama
  • Petr Kosina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7014)


This work addresses the problem of mining data stream 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-diagnosis degradations of this process, using change detection mechanisms, and self-repairs the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learns can detect re-occurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models.


Data streams concept drift meta-learning recurrent concepts 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • João Gama
    • 1
  • Petr Kosina
    • 2
  1. 1.LIAAD-INESC PortoFEP-University of PortoPortugal
  2. 2.LIAAD-INESC PortoFI Masaryk UniversityCzech Republic

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