You can only validate using data that was not included in the original classification. However, you might consider using the "Jackknife" approach. You reserve one (or a few) cases and run the classification without them, then identify them for validation. You can do this for all the cases. Check to make sure that the classification functions are not changing significantly with each run. Many classification data sets have lots of redundancy, which is needed for this approach.
Jim Palmer, SUNY ESF, Syracuse, NY
Hello,
I recently read that: you can't validate the "classification model with the data used to develop the model. You must use completely independent data otherwise you bias the results.
Is there any resampling approach to address this issue? I would be grateful if any of you can point me to some good references or studies.
Thanks for your help
Henry
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James F. Palmer, Professor
Faculty of Landscape Architecture
SUNY College of Environmental Science and Forestry
Syracuse, NY 13210
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