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Automated feature selection for a machine learning approach toward modeling a mosquito distribution

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Listed:
  • Wieland, Ralf
  • Kerkow, Antje
  • Früh, Linus
  • Kampen, Helge
  • Walther, Doreen

Abstract

This paper introduces a data science method to determine a set of features for training a vector support machine (SVM). The SVM is used to model the relationship between the distribution of one particular invasive mosquito species and climate data. Two biologists selected training data on the basis of their domain expertise. This was compared with the result of the data science simulation. The paper then explores the possible uses of data science to generate new knowledge as well as to identify the weaknesses of this technique.

Suggested Citation

  • Wieland, Ralf & Kerkow, Antje & Früh, Linus & Kampen, Helge & Walther, Doreen, 2017. "Automated feature selection for a machine learning approach toward modeling a mosquito distribution," Ecological Modelling, Elsevier, vol. 352(C), pages 108-112.
    • Handle: RePEc:eee:ecomod:v:352:y:2017:i:c:p:108-112
    DOI: 10.1016/j.ecolmodel.2017.02.029
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    References listed on IDEAS

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    1. Singer, Alexander & Johst, Karin & Banitz, Thomas & Fowler, Mike S. & Groeneveld, Jürgen & Gutiérrez, Alvaro G. & Hartig, Florian & Krug, Rainer M. & Liess, Matthias & Matlack, Glenn & Meyer, Katrin M, 2016. "Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?," Ecological Modelling, Elsevier, vol. 326(C), pages 63-74.
    2. Zeng, Yiwen & Low, Bi Wei & Yeo, Darren C.J., 2016. "Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish," Ecological Modelling, Elsevier, vol. 341(C), pages 5-13.
    3. Chen, Qiuwen & Zhang, Chengcheng & Recknagel, Friedrich & Guo, Jing & Blanckaert, Koen, 2014. "Adaptation and multiple parameter optimization of the simulation model SALMO as prerequisite for scenario analysis on a shallow eutrophic Lake," Ecological Modelling, Elsevier, vol. 273(C), pages 109-116.
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    Cited by:

    1. Wieland, Ralf & Kuhls, Katrin & Lentz, Hartmut H.K. & Conraths, Franz & Kampen, Helge & Werner, Doreen, 2021. "Combined climate and regional mosquito habitat model based on machine learning," Ecological Modelling, Elsevier, vol. 452(C).
    2. Früh, Linus & Kampen, Helge & Kerkow, Antje & Schaub, Günter A. & Walther, Doreen & Wieland, Ralf, 2018. "Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations," Ecological Modelling, Elsevier, vol. 388(C), pages 136-144.
    3. Benkendorf, Donald J. & Schwartz, Samuel D. & Cutler, D. Richard & Hawkins, Charles P., 2023. "Correcting for the effects of class imbalance improves the performance of machine-learning based species distribution models," Ecological Modelling, Elsevier, vol. 483(C).
    4. Kerkow, Antje & Wieland, Ralf & Gethmann, Jörn M. & Hölker, Franz & Lentz, Hartmut H.K., 2022. "Linking a compartment model for West Nile virus with a flight simulator for vector mosquitoes," Ecological Modelling, Elsevier, vol. 464(C).
    5. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.

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