An Adjustable Description Quality Measure for Pattern Discovery in Large Databases Using the AQ Methodology

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dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:01Z
dc.date.available2006-11-03T18:17:01Z
dc.date.issued2000-03
dc.description.abstractIn concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data contain no noise, then the primary conditions a hypothesis must satisfy are consistency and completeness with regard to the data. In real-world applications, however, data are often noisy, and the insistence on the full completeness and consistency of the hypothesis is no longer valid. In such situations, the problem is to determine a hypothesis that represents the best trade-off between completeness and consistency. This paper presents an approach to this problem in which a learner seeks rules optimizing a rule quality criterion that combines the rule coverage (a measure of completeness) and training accuracy (a measure of inconsistency). These factors are combined into a single rule quality measure through a lexicographical evaluation functional (LEF). The method has been implemented in the AQ18 learning system for natural induction and pattern discovery, and compared with several other methods. Experiments have shown that the proposed method can be easily tailored to different problems and can simulate different rule learners.by modifying the parameter of the rule quality criterion.
dc.format.extent3008 bytes
dc.format.extent104705 bytes
dc.format.mimetypetext/xml
dc.format.mimetypeapplication/pdf
dc.identifier10.1023/A:1008787919756
dc.identifier.citationKaufman, Kenneth A. and Ryszard S. Michalski. "An Adjustable Description Quality Measure for Pattern Discovery in Large Databases Using the AQ Methodology." Journal of Intelligent Information Systems 14:2 (March 2000), p. 199-216.
dc.identifier.urihttps://hdl.handle.net/1920/1463
dc.language.isoen_US
dc.relation.ispartofseriesP00-14
dc.subjectMachine learning
dc.subjectData mining
dc.subjectNatural induction
dc.subjectAQ learning
dc.subjectDecision rules
dc.titleAn Adjustable Description Quality Measure for Pattern Discovery in Large Databases Using the AQ Methodology
dc.typePostprint

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Machine Learning and Inference Laboratory, College of Public Health