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Attributional Ruletrees: A New Representation for AQ Learning

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dc.contributor.author Michalski, Ryszard S.
dc.date.accessioned 2006-11-03T18:17:20Z
dc.date.available 2006-11-03T18:17:20Z
dc.date.issued 2002-10 en_US
dc.identifier.citation Michalski, R. S., "Attributional Ruletrees: A New Representation for AQ Learning," Reports of the Machine Learning and Inference Laboratory, MLI 02-1, George Mason University, Fairfax, VA, October, 2002 (slightly edited in May, 2004). en_US
dc.identifier.uri https://hdl.handle.net/1920/1480
dc.description.abstract Attributional ruletrees are proposed as an extension of the current ruleset representation used by AQ type learning. The ruletrees split a multiclass classification problem into separate subproblems using a class splitting attribute. The resulting representation can be graphically represented as a tree whose root is assigned the class splitting attribute, branches stemming from the root are values (or sets of values) determining subsets of classes, and leaves are assigned ruleset families for classifying events to classes in these subsets. The values on the branches from the root thus define preconditions for applying ruleset families. Ruletrees are easy to interpret and understand, and can be generated by a relatively simple modification of the AQ algorithm presented below.
dc.description.sponsorship The Laboratory's research activities are supported in part by the National Science Foundation Grants No. IIS 9906858 and IIS 0097476, and in part by the UMBC/LUCITE #32 grant. en_US
dc.format.extent 1881 bytes
dc.format.extent 62264 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 02-5 en_US
dc.title Attributional Ruletrees: A New Representation for AQ Learning en_US
dc.type Technical report en_US


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