Geographic Field Data Collection: Using machine learning techniques to verify minimum data requirements for the classification task.
Steven Kirkby, Peter Eklund · Journal of Geography (Chigaku Zasshi) · 1996
Geographers and environmental scientists prefer to construct Spatial Information System (SIS) decision support from the smallest possible data. This is due to the considerable cost of ground-based surveys for data collection. This paper extends on the work of (Kirkby, 1994; Eklund et al., 1995) and reports on the use of machine learning classifiers to obtain the minimum sample size for ground-based data surveys. The study proposes a method to assess ground-based data collection using machine learning classifiers.In this domain, the inductive learning program C4.5 (Quinlan, 1993) was used to verify that a high performance classifier, better than 95 % classification accuracy on unseen data, can be constructed using 235 sample points in the study area. We compare this result to the magnitude of sample sizes required for back-propagation neural networks (NN) and instance-based learning (IBL) with the same classification accuracy on unseen data. We examine the reasons and implications for these variations for classification accuracy in this domain.