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An automated approach to the design of decision tree classifiersAn automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.
Document ID
19820036076
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Argentiero, P.
(U.S. Defense Mapping Agency Washington, DC, United States)
Chin, R.
(Defense Mapping Agency Washington, DC, United States)
Beaudet, P.
(Business and Technological Systems, Inc. Searbrook, MD, United States)
Date Acquired
August 10, 2013
Publication Date
January 1, 1982
Publication Information
Publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: PAMI-4
Subject Category
Cybernetics
Accession Number
82A19611
Funding Number(s)
CONTRACT_GRANT: NAS5-25835
Distribution Limits
Public
Copyright
Other

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