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

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dc.creator Malero, Augustine
dc.date 2021-05-12T07:51:43Z
dc.date 2021-05-12T07:51:43Z
dc.date 2014
dc.date.accessioned 2022-10-20T13:47:44Z
dc.date.available 2022-10-20T13:47:44Z
dc.identifier Malero, 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.identifier 2345-3397
dc.identifier DOI: https://doi.org/10.5897/IJBC2019.1267
dc.identifier http://hdl.handle.net/20.500.12661/3031
dc.identifier.uri http://hdl.handle.net/20.500.12661/3031
dc.description Abstract. Full text article available at https://doi.org/10.5897/IJBC2019.126
dc.description Advances 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.language en
dc.publisher International Journal of Computer Science & Network Solutions (IJCSNS)
dc.subject Spam filtering
dc.subject Machine learning
dc.subject Lemmatization
dc.subject Power transformation
dc.subject Term frequency
dc.subject Information Communication Technology
dc.subject ICT
dc.subject Electronic mail
dc.title Applying feature transformation using relative frequency with power transformation and lemmatization in automatic spam filtering
dc.type Article


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