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

dc.contributor.authorMalero, Augustine
dc.date.accessioned2021-05-12T07:51:43Z
dc.date.available2021-05-12T07:51:43Z
dc.date.issued2014
dc.descriptionAbstract. Full text article available at https://doi.org/10.5897/IJBC2019.126en_US
dc.description.abstractAdvances 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.en_US
dc.identifier.citationMalero, 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), 21-27en_US
dc.identifier.issn2345-3397
dc.identifier.otherDOI: https://doi.org/10.5897/IJBC2019.1267
dc.identifier.urihttp://hdl.handle.net/20.500.12661/3031
dc.language.isoenen_US
dc.publisherInternational Journal of Computer Science & Network Solutions (IJCSNS)en_US
dc.subjectSpam filteringen_US
dc.subjectMachine learningen_US
dc.subjectLemmatizationen_US
dc.subjectPower transformationen_US
dc.subjectTerm frequencyen_US
dc.subjectInformation Communication Technologyen_US
dc.subjectICTen_US
dc.subjectElectronic mailen_US
dc.titleApplying feature transformation using relative frequency with power transformation and lemmatization in automatic spam filteringen_US
dc.typeArticleen_US
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