CLOUD CLASSIFICATION USING ERRORCORRECTING OUTPUT CODES
David W. Aha, R. Bankert · 1997
Novel artificial intelligence methods are used to classify 16x16 pixel regions (obtained from Advanced Very High Resolution Radiometer (AVHRR) images) in terms of cloud type (e.g., stratus, cumulus, etc.). We previously 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 matrices revealed that a small number of confusable classes (e.g., cirrus and cirrostratus) dominated the classification errors. We conjectured that, 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 class label with a set of error-correcting output codes, a general technique applicable to any classification algorithm for tasks with at least three classes. Our initial results are promising; error correcting codes significa...