Evaluation Of Linear Interpolation Smoothing On Naive Bayes Spam Classifier

dc.contributor.authorAdewole, A.P.
dc.contributor.authorFakorede, O.J.
dc.contributor.authorAkwuegbo, S.O.N.
dc.date.accessioned2019-09-09T17:08:24Z
dc.date.available2019-09-09T17:08:24Z
dc.date.issued2014
dc.description.abstractThe inconvenience associated with spams and the cost of having an important mail misclassified as spam have made all efforts at improving spam filtering worthwhile. The Naive Bayes algorithm has been found to be successful in properly classifying mails. However, they are not perfect. Recent researches have introduced the idea of smoothing into the Naive Bayes algorithm and they have been found to produce better classification. This study applies the concept of linear interpolation smoothing to Naive Bayes spam classification. The resulting classifier did well at improving spam classification and also reducing false positives.en_US
dc.identifier.citationAdewole A.P, Fakorede O.J, and Akwuegbo S.O.N. (2014). Evaluation Of Linear Interpolation Smoothing On Naive Bayes Spam Classifier. INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH. 2(6):143-146en_US
dc.identifier.issn2347-4289
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/5423
dc.language.isoenen_US
dc.publisherINTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCHen_US
dc.relation.ispartofseries2;6
dc.subjectNaïve Bayesen_US
dc.subjectSmoothingen_US
dc.subjectLinear Interpolationen_US
dc.subjectSpamen_US
dc.subjectHam False Positivesen_US
dc.subjectFalse Negativesen_US
dc.titleEvaluation Of Linear Interpolation Smoothing On Naive Bayes Spam Classifieren_US
dc.typeArticleen_US
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