Machine learning of poorly predictable ecological data

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@Article{Shan:2006:EM,

• author = "Y. Shan and D. Paull and R. I. McKay",
• title = "Machine learning of poorly predictable ecological data",
• journal = "Ecological Modelling",
• year = "2006",
• volume = "195",
• number = "1-2",
• pages = "129--138",
• month = "15 " # may,
• note = "Selected Papers from the Third Conference of the International Society for Ecological Informatics (ISEI), August 26--30, 2002, Grottaferrata, Rome, Italy",
• keywords = "genetic algorithms, genetic programming, Decision trees, Neural networks, Support vector machines, Southern brown bandicoot, Spatial distribution modelling",
• DOI = "doi:10.1016/j.ecolmodel.2005.11.015",
• abstract = "a variety of machine learning techniques to a difficult modelling problem, the spatial distribution of an endangered Australian marsupial, the southern brown bandicoot (Isoodon obesulus). Four learning techniques decision trees/rules, neural networks, support vector machines and genetic programming were applied to the problem. Support vector and neural network approaches gave marginally better predictivity, but in the context of low overall accuracy, decision trees and genetic programming gave more useful results because of the human comprehensibility of their models.",

}

Genetic Programming entries for Yin Shan David Paull R I (Bob) McKay

Citations