Applying feature transformation using relative frequency with power transformation and lemmatization in automatic spam filtering

dc.creatorMalero, Augustine
dc.date2021-05-12T07:51:43Z
dc.date2021-05-12T07:51:43Z
dc.date2014
dc.date.accessioned2022-10-20T13:47:44Z
dc.date.available2022-10-20T13:47:44Z
dc.descriptionAbstract. Full text article available at https://doi.org/10.5897/IJBC2019.126
dc.descriptionAdvances in Information and communication technology have paved a way for electronic mail commonly referred as email to become the medium of communication. Over the recent years this medium has become the target of abuse through spamming. One of the approaches of combating spamming is the use of automatic spam filtering through machine learning. The conventional features in automatic spam filtering are Term Frequency with Inverse Document Frequency (TFIDF). In this paper, an alternative approach is presented with the use of Relative Frequency with Power Transformation (RFPT) coupled with lemmatization technique. The techniques used considerably show improvements over the conventional one that is TFIDF.
dc.identifierMalero, A. (2014). Applying feature transformation using relative frequency with power transformation and lemmatization in automatic spam filtering. International Journal of Computer Science & Network Solutions, 2(10), 21-27
dc.identifier2345-3397
dc.identifierDOI: https://doi.org/10.5897/IJBC2019.1267
dc.identifierhttp://hdl.handle.net/20.500.12661/3031
dc.identifier.urihttp://hdl.handle.net/20.500.12661/3031
dc.languageen
dc.publisherInternational Journal of Computer Science & Network Solutions (IJCSNS)
dc.subjectSpam filtering
dc.subjectMachine learning
dc.subjectLemmatization
dc.subjectPower transformation
dc.subjectTerm frequency
dc.subjectInformation Communication Technology
dc.subjectICT
dc.subjectElectronic mail
dc.titleApplying feature transformation using relative frequency with power transformation and lemmatization in automatic spam filtering
dc.typeArticle

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