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Recurrent concepts in data streams classification

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Abstract

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.

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Notes

  1. The default value is 250 examples.

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Acknowledgments

This work is part-funded by the ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness), by the Portuguese Funds through the FCT (Portuguese Foundation for Science and Technology) within project FCOMP—01-0124-FEDER-022701. The authors acknowledge the financial support given by the project Knowledge Discovery from Ubiquitous Data Streams (PTDC/EIA/098355/2008), funded by FCT. Petr Kosina acknowledges the support of Masaryk University, Faculty of Informatics.

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Authors and Affiliations

  1. LIAAD-INESC TEC, Porto, Portugal

    João Gama & Petr Kosina

  2. FEP-University of Porto, Porto, Portugal

    João Gama

  3. FI Masaryk University, Brno, Czech Republic

    Petr Kosina

Authors
  1. João Gama
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  2. Petr Kosina
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Correspondence to João Gama.

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Gama, J., Kosina, P. Recurrent concepts in data streams classification. Knowl Inf Syst 40, 489–507 (2014). https://doi.org/10.1007/s10115-013-0654-6

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