Article,
Induction of a marsupial density model using genetic programming and spatial relationships
Ecological Modelling, 131 (2-3): 299--317 (2000)
DOI: doi:10.1016/S0304-3800(00)00248-9
Abstract
Machine learning techniques have been developed that
allow the induction of spatial models for the
prediction of habitat types and population
distribution. However, most learning approaches are
based on a propositional language for the development
of models and therefore cannot express a wide range of
possible spatial relationships that exist in the data.
This paper compares the application of a functional
evolutionary machine learning technique for prediction
of marsupial density to some standard machine learning
techniques. The ability of the learning system to
express spatial relationships allows an improved
predictive model to be developed, which is both
parsimonious and understandable. Additionally, the maps
produced from this approach have a generalised
appearance of the measured glider density, suggesting
that the underlying preferred habitat properties of the
greater glider have been identified.
