Encoding heuristic knowledge for GIS classification

This paper discusses a sequence of experiments comparing classification results produced by geographic information systems (GIS) to those produced by integrated GIS/expert systems (ES). Analysis is conducted via the use of three machine learning techniques: inductive learning, instance based learning and neural networks. The paper argues that inferior knowledge engineering techniques result in poor heuristic knowledge encoding in both traditional GIS and more recent GIS/ES classifications.