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dc.contributor.authorHadzic, Fedja
dc.contributor.authorDillon, Tharam S.
dc.contributor.editorAna Fred
dc.contributor.editorJoaquim Filipe
dc.contributor.editorHugo Gamboa
dc.date.accessioned2017-01-30T14:58:47Z
dc.date.available2017-01-30T14:58:47Z
dc.date.created2009-02-17T18:01:52Z
dc.date.issued2008
dc.identifier.citationHadzic, Fedja and Dillon, Tharam. 2008. Human-like rule optimization for continuous domains, in Fred, A. and Filipe. J. and Gamboa , H.(ed), Biomedical Engineering Systems and Technologies. pp. 330-343. Heidelberg, Germany: Springer.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/42310
dc.identifier.doi10.1007/978-3-540-92219-3_25
dc.description.abstract

When using machine learning techniques for data mining purposes one of the main requirements is that the learned rule set is represented in a comprehensible form. Simpler rules are preferred as they are expected to perform better on unseen data. At the same time the rules should be specific enough so that the misclassification rate is kept to a minimum. In this paper we present a rule optimizing technique motivated by the psychological studies of human concept learning. The technique allows for reasoning to happen at both higher levels of abstraction and lower level of detail in order to optimize the rule set. Information stored at the higher level allows for optimizing processes such as rule splitting, merging and deleting, while the information stored at the lower level allows for determining the attribute relevance for a particular rule. The attributes detected as irrelevant can be removed and the ones previously detected as irrelevant can be reintroduced if necessary. The method is evaluated on the rules extracted from publicly available real world datasets using different classifiers, and the results demonstrate the effectiveness of the presented rule optimizing technique.

dc.publisherSpringer
dc.subjectFeature Selection
dc.subjectData Mining
dc.subjectRule Optimization
dc.titleHuman-like rule optimization for continuous domains
dc.typeBook Chapter
dcterms.source.startPage330
dcterms.source.endPage343
dcterms.source.titleBiomedical Engineering Systems and Technologies
dcterms.source.isbn9783540922186
dcterms.source.placeHeidelberg, Germany
dcterms.source.chapter40
curtin.note

The original publication is available at www.springerlink.com

curtin.departmentCentre for Extended Enterprises and Business Intelligence
curtin.accessStatusOpen access
curtin.facultyCurtin Business School
curtin.facultySchool of Information Systems


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