Using Learning of Behavior Rules to Mine Medical Data for Sequence Rules

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dc.contributor.authorDenzinger, Jorgeng
dc.contributor.authorGao, Jieeng
dc.date.accessioned2008-02-27T16:58:41Z
dc.date.available2008-02-27T16:58:41Z
dc.date.computerscience2004-03-02eng
dc.date.issued2004-03-02eng
dc.description.abstractIn fields like medical care the temporal relations in the records (transactions) are of great help for identifying a particular group of cases. Thus there is some need for sequence rule learning in the classification problems in these fields. In this paper, a genetic algorithm for sequence rule learning is presented based on concepts from learning behavior of agents. The algorithm employs a Michigan-like approach to evolve a group of sequence rules, and extracts good ones into the result sequence rule set from time to time. It contains a novel quality-based intelligent genetic operator, and many adaptive enhancements to make implicit use of data-set-specific knowledge. The algorithm is evaluated on a real-world medical data set from the PKDD 99 Challenge. The results indicate that the algorithm can get satisfactory sequence rule sets from the sparse and noisy data set.eng
dc.description.notesWe are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.caeng
dc.identifier.department2004-739-04eng
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/30582
dc.identifier.urihttp://hdl.handle.net/1880/45847
dc.language.isoEngeng
dc.publisher.corporateUniversity of Calgaryeng
dc.publisher.facultyScienceeng
dc.subjectComputer Scienceeng
dc.titleUsing Learning of Behavior Rules to Mine Medical Data for Sequence Ruleseng
dc.typeunknown
thesis.degree.disciplineComputer Scienceeng
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