Representing and reducing error in natural-resource classification using model combination.
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]
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|Source:||International Journal of Geographical Information Science|
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