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Representing and reducing error in natural-resource classification using model combination.

Huang, Zhi; Lees, Brian G

Description

Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster - Shafer's Theory of Evidence have long claimed to be more error-tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Journal article
URI: http://hdl.handle.net/1885/85228
Source: International Journal of Geographical Information Science
DOI: 10.1080/13658810500032446

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