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Learning ensembles of population dynamics models and their application to modelling aquatic ecosystems

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  • Simidjievski, Nikola
  • Todorovski, Ljupčo
  • Džeroski, Sašo

Abstract

Ensemble methods are machine learning methods that construct a set of models and combine their outputs into a single prediction. The models within an ensemble can have different structure and parameters and make diverse predictions. Ensembles achieve high predictive performance, benefiting from the diversity of the individual models and outperforming them.

Suggested Citation

  • Simidjievski, Nikola & Todorovski, Ljupčo & Džeroski, Sašo, 2015. "Learning ensembles of population dynamics models and their application to modelling aquatic ecosystems," Ecological Modelling, Elsevier, vol. 306(C), pages 305-317.
    • Handle: RePEc:eee:ecomod:v:306:y:2015:i:c:p:305-317
    DOI: 10.1016/j.ecolmodel.2014.08.019
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    References listed on IDEAS

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    1. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
    2. Čerepnalkoski, Darko & Taškova, Katerina & Todorovski, Ljupčo & Atanasova, Nataša & Džeroski, Sašo, 2012. "The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems," Ecological Modelling, Elsevier, vol. 245(C), pages 136-165.
    3. Tashkova, Katerina & Šilc, Jurij & Atanasova, Nataša & Džeroski, Sašo, 2012. "Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization," Ecological Modelling, Elsevier, vol. 226(C), pages 36-61.
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    Cited by:

    1. Nikola Simidjievski & Ljupčo Todorovski & Sašo Džeroski, 2016. "Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-27, April.

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