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On the Creation of Diverse Ensembles for Nonstationary Environments Using Bio-inspired Heuristics

  • Jesus L. Lobo
  • Javier Del Ser
  • Esther Villar-Rodriguez
  • Miren Nekane Bilbao
  • Sancho Salcedo-Sanz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 514)

Abstract

Recently the relevance of adaptive models for dynamic data environments has turned into a hot topic due to the vast number of scenarios generating nonstationary data streams. When a change (concept drift) in data distribution occurs, the ensembles of models trained over these data sources are obsolete and do not adapt suitably to the new distribution of the data. Although most of the research on the field is focused on the detection of this drift to re-train the ensemble, it is widely known the importance of the diversity in the ensemble shortly after the drift in order to reduce the initial drop in accuracy. In a Big Data scenario in which data can be huge (and also the number of past models), achieving the most diverse ensemble implies the calculus of all possible combinations of models, which is not an easy task to carry out quickly in the long term. This challenge can be formulated as an optimization problem, for which bio-inspired algorithms can play one of the key roles in these adaptive algorithms. Precisely this is the goal of this manuscript: to validate the relevance of the diversity right after drifts, and to unveil how to achieve a highly diverse ensemble by using a self-learning optimization technique.

Keywords

Concept drift Diversity Bioinspired optimization 

Notes

Acknowledgments

This work has been supported by the Basque Government through the ELKARTEK program (ref. KK-2015/0000080, BID3A project) and BID3ABI project.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Jesus L. Lobo
    • 1
  • Javier Del Ser
    • 1
    • 2
    • 3
  • Esther Villar-Rodriguez
    • 1
  • Miren Nekane Bilbao
    • 2
  • Sancho Salcedo-Sanz
    • 4
  1. 1.TECNALIADerioSpain
  2. 2.University of the Basque Country UPV/EHUBilbaoSpain
  3. 3.Basque Center for Applied Mathematics (BCAM)BilbaoSpain
  4. 4.Universidad de AlcaláAlcalá de HenaresSpain

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