Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Sara Silva and Vijay Ingalalli and Susana Vinga and 
                 Joao M. B. Carreiras and Joana B. Melo and 
                 Mauro Castelli and Leonardo Vanneschi and Ivo Goncalves and 
                 Jose Caldas",
  title =        "Prediction of Forest Aboveground Biomass: An Exercise
                 on Avoiding Overfitting",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "407--417",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_41",
  size =         "11 pages",
  abstract =     "Mapping and understanding the spatial distribution of
                 forest above ground biomass (AGB) is an important and
                 challenging task. This paper describes an exercise of
                 predicting the forest AGB of Guinea-Bissau, West
                 Africa, using synthetic aperture radar data and
                 measurements of tree size collected in field campaigns.
                 Several methods were attempted, from linear regression
                 to different variants and techniques of Genetic
                 Programming (GP), including the cutting edge geometric
                 semantic GP approach. The results were compared between
                 each other in terms of root mean square error and
                 correlation between predicted and expected values of
                 AGB. None of the methods was able to produce a model
                 that generalises well to unseen data or significantly
                 outperforms the model obtained by the state-of-the-art
                 methodology, and the latter was also not better than a
                 simple linear model. We conclude that the AGB
                 prediction is a difficult problem, aggravated by the
                 small size of the available data set.",
  notes =        "
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",

Genetic Programming entries for Sara Silva Vijay Ingalalli Susana de Almeida Mendes Vinga Martins Joao M B Carreiras Joana B Melo Mauro Castelli Leonardo Vanneschi Ivo Goncalves Jose Miguel Ranhada Vellez Caldas