Novel artificial intelligence methods are used to classify 16x16 pixel
regions (obtained from Advanced Very High Resolution Radiometer (AVHR
R) images) in terms of cloud type (e.g., stratus, cumulus). We previou
sly reported that intelligent feature selection methods, combined with
nearest neighbor classifiers, can dramatically improve classification
accuracy on this task. Our subsequent analyses of the confusion matri
ces revealed that a small number of confusable classes (e.g., cirrus a
nd cirrostratus) dominated the classification errors. We conjectured t
hat, if the class labels in the data were re-represented so that these
cloud classes are more easily distinguished, then additional accuracy
gains might result. We explored this hypothesis by replacing each cla
ss label with a set of error-correcting output codes, a general techni
que applicable to any classification algorithm for tasks with at least
three classes. Our initial results are promising; error correcting co
des significantly increased classification accuracy compared with usin
g standard representations for class labels. To our knowledge, this is
the first successful integration of a k-nearest neighbor classifier a
nd error-correcting output codes (i.e., where k is, effectively, small
). One conclusion is that environmental scientists should always selec
t, for their classification tasks, a classifier that reduces both vari
ance and learning bias.