Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/49373
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dc.coverage.spatialEffective multiple criterion approach for building a rule based decision tree classifieren_US
dc.date.accessioned2015-09-11T04:51:09Z-
dc.date.available2015-09-11T04:51:09Z-
dc.date.issued2015-09-11-
dc.identifier.urihttp://hdl.handle.net/10603/49373-
dc.description.abstractThe reliability of disease classification for medical dataset has newlinebecome challenging task due to the measurement of noise and biological newlineheterogeneity among patients Most of the medical datasets have complicated newlineboundaries between attributes and classes The current classification methods newlinefind only the rules with high accuracy These methods either cover only a newlinenarrow part of the objects or require numerous attributes to explain a newlineclassification rule Although these methods are computationally effective for newlinerealizing the classifications rules there are heuristic approaches that can newlineinduce feasible rules Feature selection refers to the problem of selecting those newlineinput attributes that are most predictive for a given outcome a problem newlineencountered in many areas such as machine learning pattern recognition and newlinesignal processin newline newlineen_US
dc.format.extentxix,180p.en_US
dc.languageEnglishen_US
dc.relationp.167-179en_US
dc.rightsuniversityen_US
dc.titleEffective multiple criterion approach for building a rule based decision tree classifieren_US
dc.title.alternativeen_US
dc.creator.researcherYamini Cen_US
dc.subject.keyworddecision tree classifieren_US
dc.subject.keywordmultiple criterionen_US
dc.subject.keywordScience and humanitiesen_US
dc.description.noteReference p.167-179en_US
dc.contributor.guidePunithavalli Men_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Science and Humanitiesen_US
dc.date.registeredn.d.en_US
dc.date.completed01/08/2014en_US
dc.date.awarded30/08/2014en_US
dc.format.dimensions23cmen_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Faculty of Science and Humanities

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01_title.pdfAttached File16.22 kBAdobe PDFView/Open
02_certificate.pdf5.99 kBAdobe PDFView/Open
03_abstract.pdf13.1 kBAdobe PDFView/Open
04_acknowledgement.pdf6.58 kBAdobe PDFView/Open
05_contents.pdf27.77 kBAdobe PDFView/Open
06_chapter 1.pdf178.98 kBAdobe PDFView/Open
07_chapter 2.pdf137.99 kBAdobe PDFView/Open
08_chapter 3.pdf178.48 kBAdobe PDFView/Open
09_chapter 4.pdf415.64 kBAdobe PDFView/Open
10_chapter 5.pdf343.76 kBAdobe PDFView/Open
11_chapter 6.pdf368.04 kBAdobe PDFView/Open
12_chapter 7.pdf81.05 kBAdobe PDFView/Open
13_chapter 8.pdf28.04 kBAdobe PDFView/Open
14_references.pdf59.13 kBAdobe PDFView/Open
15_publications.pdf20.5 kBAdobe PDFView/Open


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