Attributional Ruletrees: A New Representation for AQ Learning

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dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:20Z
dc.date.available2006-11-03T18:17:20Z
dc.date.issued2002-10
dc.description.abstractAttributional 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.sponsorshipThe 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.
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dc.format.extent62264 bytes
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dc.format.mimetypeapplication/pdf
dc.identifier.citationMichalski, 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).
dc.identifier.urihttps://hdl.handle.net/1920/1480
dc.language.isoen_US
dc.relation.ispartofseriesP 02-5
dc.titleAttributional Ruletrees: A New Representation for AQ Learning
dc.typeTechnical report

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