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Article: AUTOMATED APPROACH TO THE DESIGN OF DECISION TREE CLASSIFIERS.

TitleAUTOMATED APPROACH TO THE DESIGN OF DECISION TREE CLASSIFIERS.
Authors
Issue Date1982
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tpami
Citation
Ieee Transactions On Pattern Analysis And Machine Intelligence, 1982, v. PAMI-4 n. 1, p. 51-57 How to Cite?
AbstractThe classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data causes a significant computational problem. Decision tree classification is a popular approach to the problem. This type of classifier is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. If a decision tree classifier is well designed, the result in many cases is a classification scheme which is accurate, flexible, and computationally efficient. This work provides an automated technique for effective decision tree design which relies only on a priori statistics. This procedure utilizes canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classification is also provided.
Persistent Identifierhttp://hdl.handle.net/10722/178129
ISSN
2015 Impact Factor: 6.077
2015 SCImago Journal Rankings: 7.653

 

DC FieldValueLanguage
dc.contributor.authorArgentiero, Peteren_US
dc.contributor.authorChin, Rolanden_US
dc.contributor.authorBeaudet, Paulen_US
dc.date.accessioned2012-12-19T09:43:01Z-
dc.date.available2012-12-19T09:43:01Z-
dc.date.issued1982en_US
dc.identifier.citationIeee Transactions On Pattern Analysis And Machine Intelligence, 1982, v. PAMI-4 n. 1, p. 51-57en_US
dc.identifier.issn0162-8828en_US
dc.identifier.urihttp://hdl.handle.net/10722/178129-
dc.description.abstractThe classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data causes a significant computational problem. Decision tree classification is a popular approach to the problem. This type of classifier is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. If a decision tree classifier is well designed, the result in many cases is a classification scheme which is accurate, flexible, and computationally efficient. This work provides an automated technique for effective decision tree design which relies only on a priori statistics. This procedure utilizes canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classification is also provided.en_US
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tpamien_US
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.titleAUTOMATED APPROACH TO THE DESIGN OF DECISION TREE CLASSIFIERS.en_US
dc.typeArticleen_US
dc.identifier.emailChin, Roland: rchin@hku.hken_US
dc.identifier.authorityChin, Roland=rp01300en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0019914557en_US
dc.identifier.volumePAMI-4en_US
dc.identifier.issue1en_US
dc.identifier.spage51en_US
dc.identifier.epage57en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridArgentiero, Peter=6603589576en_US
dc.identifier.scopusauthoridChin, Roland=7102445426en_US
dc.identifier.scopusauthoridBeaudet, Paul=6603477658en_US

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