Induction of a marsupial density model using genetic programming and spatial relationships

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.