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

© 1995 IEEE. All rights reserved. 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 [9, 2] 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 [7] 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.