A Hybrid Method for Feature Construction and Selection to Improve Wind-damage Prediction in the Forestry Sector

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Hart:2017:GECCO,
  author =       "Emma Hart and Kevin Sim and Barry Gardiner and 
                 Kana Kamimura",
  title =        "A Hybrid Method for Feature Construction and Selection
                 to Improve Wind-damage Prediction in the Forestry
                 Sector",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  pages =        "1121--1128",
  address =      "Berlin, Germany",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming,
                 feature-construction, forestry, machine-learning",
  isbn13 =       "978-1-4503-4920-8",
  URL =          "http://www.human-competitive.org/sites/default/files/hart-paper.pdf",
  URL =          "http://doi.acm.org/10.1145/3071178.3071217",
  DOI =          "doi:10.1145/3071178.3071217",
  acmid =        "3071217",
  size =         "8 pages",
  abstract =     "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 South-West 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.",
  notes =        "Bronze Winner 2018 HUMIES

                 Also known as \cite{Hart:2017:HMF:3071178.3071217}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Emma Hart Kevin Sim Barry Gardiner Kana Kamimura

Citations