Machine learning classifier performance as an indicator for data acquisition regimes in geographical field surveys

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 the work of (Zhang and Giardino, 1992) and (Eklund et al., 1994) and reports on the use of machine learning classifiers to obtain the minimum sample size for ground-based data surveys. The study of machine learning algorithms proposes a method to assess ground-based data collection using machine learning classifiers. In this domain, the inductive learning program C4.5 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 backpropagation 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