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Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/16090
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dc.contributor.authorAmeen, A.O.-
dc.contributor.authorOlagunju, M.-
dc.contributor.authorAwotunde, J.B.-
dc.contributor.authorAdebakin, T,O,-
dc.contributor.authorAlabi, I.O.-
dc.date.accessioned2022-12-26T03:38:42Z-
dc.date.available2022-12-26T03:38:42Z-
dc.date.issued2017-06-
dc.identifier.citation6. Ameen A. O., Olagunju M., Awotunde, J. B., Adelakin, T. O. & Alabi, I.O. (2017). Performance evaluation of breast cancer diagnosis using radial basis function, C4.5 and Adaboost. University of Pitesti scientific bulletin electronics and computer science, 17 (2), 1-12.en_US
dc.identifier.issn2344 – 2166-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/16090-
dc.description.abstractT his paper conducted a performance evaluation on the most commonly data mining algorithms: Support Vector Machines (Radial basis function), C4.5 decision tree algorithm and Adaboost, using the two previous algorithms as base classifiers (ensemble approach), on breast cancer diagnostic removing redundant or irrelevant features using Chi-square. Result shows that while C4.5 builds its classification model in a short time, The Adaboost with SVM as its base classifieren_US
dc.language.isoenen_US
dc.publisherEditura Universitatii din Pitestien_US
dc.relation.ispartofseriesElectronics and Computers Science;Vol 17, Issue 2-
dc.subjectBreast cancer diagnosis,en_US
dc.subjectClassification algorithmen_US
dc.subjectExpert Systemen_US
dc.subjectRadial basis functionen_US
dc.subjectSupport vector machinesen_US
dc.subjectData miningen_US
dc.titlePERFORMANCE EVALUATION OF BREAST CANCER DIAGNOSIS USING RADIAL BASIS FUNCTION, C4.5 AND ADABOOSTen_US
dc.typeArticleen_US
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