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Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider

Author

Listed:
  • Yong-Chao Su

    (Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

  • Cheng-Yu Wu

    (Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

  • Cheng-Hong Yang

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
    Ph. D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
    Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

  • Bo-Sheng Li

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Sin-Hua Moi

    (Center of Cancer Program Development, E-Da Cancer Hospital, I-Shou University, Kaohsiung 82445, Taiwan)

  • Yu-Da Lin

    (Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu 880011, Taiwan)

Abstract

Cost–benefit analysis is widely used to elucidate the association between foraging group size and resource size. Despite advances in the development of theoretical frameworks, however, the empirical systems used for testing are hindered by the vagaries of field surveys and incomplete data. This study developed the three approaches to data imputation based on machine learning (ML) algorithms with the aim of rescuing valuable field data. Using 163 host spider webs (132 complete data and 31 incomplete data), our results indicated that the data imputation based on random forest algorithm outperformed classification and regression trees, the k -nearest neighbor, and other conventional approaches (Wilcoxon signed-rank test and correlation difference have p -value from < 0.001–0.030). We then used rescued data based on a natural system involving kleptoparasitic spiders from Taiwan and Vietnam ( Argyrodes miniaceus , Theridiidae) to test the occurrence and group size of kleptoparasites in natural populations. Our partial least-squares path modelling (PLS-PM) results demonstrated that the size of the host web ( T = 6.890, p = 0.000) is a significant feature affecting group size. The resource size ( T = 2.590, p = 0.010) and the microclimate ( T = 3.230, p = 0.001) are significant features affecting the presence of kleptoparasites. The test of conformation of group size distribution to the ideal free distribution (IFD) model revealed that predictions pertaining to per-capita resource size were underestimated (bootstrap resampling mean slopes

Suggested Citation

  • Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:415-:d:502614
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    References listed on IDEAS

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    1. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    2. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    3. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    4. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    5. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    6. Křivan, Vlastimil & Cressman, Ross & Schneider, Candace, 2008. "The ideal free distribution: A review and synthesis of the game-theoretic perspective," Theoretical Population Biology, Elsevier, vol. 73(3), pages 403-425.
    7. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    8. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    9. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    10. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
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