Research Output

A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector

  Catastrophic damage to forests resulting from major storms has resulted in serious timber and financial losses within the sector across Europe in the recent past. Developing risk assessment methods is thus one of the keys to finding forest management strategies to reduce future damage. Previous approaches to predicting damage to individual trees have used mechanistic models of wind-flow or logistical regression with mixed results. We propose a novel filter-based Genetic Programming method for constructing a large set of new features which are ranked using the Hellinger distance metric which is insensitive to skew in the data. A wrapper-based feature-selection method that uses a random forest classifier is then applied predict damage to individual trees. Using data collected from two forests within SouthWest France, we demonstrate significantly improved classification results using the new features, and in comparison to previously published results. The feature-selection method retains a small set of relevant variables consisting only of newly constructed features whose components provide insights that can inform forest management policies.

  • Date:

    01 January 2017

  • Publication Status:

    Published

  • Publisher

    Association for computing machinery

  • DOI:

    10.1145/3071178.3071217

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    Edinburgh Napier Funded

Citation

Hart, E., Sim, K., Gardiner, B., & Kamimura, K. (2017). A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector. In GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference, 1121-1128. doi:10.1145/3071178.3071217

Authors

Keywords

Computing methodologies, search methodologies, genetic programming, KEYWORDS Feature-construction, Machine-Learning, Forestry

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