Comparative Analysis of Selected Supervised Classification Algorithms.

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dc.contributor.authorMabayoje, M.A
dc.contributor.authorBalogun, A.O
dc.contributor.authorSalihu, S.
dc.contributor.authorOladipupo, K.R
dc.date.accessioned2018-11-30T12:40:40Z
dc.date.available2018-11-30T12:40:40Z
dc.date.issued2015
dc.description.abstractInformation is not packaged in a standard easy-to-retrieve format. It is an underlying and usually subtle and misleading concept buried in massive amounts of raw data. From the beginning of time it has been man’s common goal to make his life easier. The prevailing notion in society is that wealth brings comfort and luxury, so it is not surprising that there has been so much work done on ways to sort large volume of data. Over the year, there are various data mining techniques and used to sort large volume of data. This paper considers Classification which is a supervised learning technique. Therefore the need to come up with the most efficient way to deal with voluminous data with very little time frame has been one of the biggest challenges to the AI community. Hence, this paper presents a comparative analysis of three classification algorithms namely; Decision Tree (J-48), Random Forest and Naïve Bayes. A 10-fold cross validation technique is used for the performance evaluation of the classifiers on KDD’’99, VOTE and CREDIT datasets using WEKA (Waikato Environment for Knowledge Analysis) tool. The experiment shows that the type of dataset determines which classifier is suitable.en_US
dc.identifier.issn2006-1781
dc.identifier.urihttp://hdl.handle.net/123456789/1283
dc.language.isoenen_US
dc.publisherAfrican Journal of Computing & ICTen_US
dc.subjectClassificationen_US
dc.subjectDecision Tree (DT J-48)en_US
dc.subjectRandom Forest (RF)en_US
dc.subjectNaïve Bayes (NB)en_US
dc.titleComparative Analysis of Selected Supervised Classification Algorithms.en_US
dc.typeArticleen_US

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