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Boosting, Bagging and Ensembles in the Real World: An Overview, some Explanations and a Practical Synthesis for Holistic Global Wildlife Conservation Applications Based on Machine Learning with Decision Trees

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Machine Learning for Ecology and Sustainable Natural Resource Management

Abstract

Boosting, bagging and ensembles are intellectually ‘deep’ modeling methods well-known and described for several decades. Great computing tools exist to use those methods. But with few exceptions they have not been used well for natural resource conservation management or ecology; for instance, the advanced works of Breiman (2001), Friedman (2001), and Elder (2003) still await generic recognition. Here I present on these methods, conveniently driven by binary recursive partitioning (Classification and Regression Trees CARTs), and many of their real-world aspects and usages. I elaborate on applications and on some of the implementation hurdles known. It is shown that those machine learning methods are the essential part of the new generation of quantitative reasoning. It allows for relevant progress, all while the global environmental state decays further, climate change remain unaccounted for and sustainability policies remain outdated urging for an effective change of global culture and governance.

“My goal is simple. It is a complete understanding of the universe, why it is as it is and why it exists at all.”

Stephen Hawkins

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Acknowledgement

I thank Profs R. O’Connor and A.W. (Tony) Diamond for an early workshop on statistics with ACWERN at UNB, Canada introducing me in the late 1990s to tree-based techniques (CART) and multivariate analysis. I thank Dan Steinberg and Salford Systems Ltd. for a workshop with U.S. IALE at Snowbird, Utah, as well as with The Wildlife Society, Alaska Chapter, for a wider debate and introduction of tree-based methods, boosting and bagging. I am indebted to U.S.IALE, the Global Primate Network in Kathmandu, Nepal, Medical University Taipeh, Taiwan, and the Wildlife Institute of India in Dheradun for their workshop promotion and support. Thanks to S. Linke, I. Presse, B. Walter, G. Regmi, M. Suwal, R. Lama, C. Cambu, H. Hera, S. Sparks, Y. Subaru, H. Berrios and the many members of the -EWHALE lab- at UAF for their discussions and partly, support. This is EWHALE lab publication #187.

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Authors and Affiliations

  1. EWHALE Lab, Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska-Fairbanks, Fairbanks, AK, USA

    Falk Huettmann

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  1. Falk Huettmann
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  1. Black Bawks Data Science Ltd, Fort Augustus, Scotland, New Zealand

    Grant Humphries

  2. U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, USA

    Dawn R. Magness

  3. Institute of Arctic Biology, University of Alaska - Fairbanks, FAIRBANKS, AK, USA

    Falk Huettmann

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Huettmann, F. (2018). Boosting, Bagging and Ensembles in the Real World: An Overview, some Explanations and a Practical Synthesis for Holistic Global Wildlife Conservation Applications Based on Machine Learning with Decision Trees. In: Humphries, G., Magness, D., Huettmann, F. (eds) Machine Learning for Ecology and Sustainable Natural Resource Management. Springer, Cham. https://doi.org/10.1007/978-3-319-96978-7_3

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