Treffer: Spam Detection Using Machine Learning

Title:
Spam Detection Using Machine Learning
Authors:
Agboola, Olubodunde Stephen
Source:
Computer Engineering and Intelligent Systems; Vol 11, No 3 (2020); 33-41
Publisher Information:
The International Institute for Science, Technology and Education (IISTE)
Publication Year:
2020
Collection:
International Institute for Science, Technology and Education (IISTE): E-Journals
Document Type:
Fachzeitschrift article in journal/newspaper
File Description:
application/pdf
Language:
English
Relation:
https://www.iiste.org/Journals/index.php/CEIS/article/view/52548/54308; https://www.iiste.org/Journals/index.php/CEIS/article/view/52548
Availability:
https://www.iiste.org/Journals/index.php/CEIS/article/view/52548
Rights:
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication. Copyrights for articles published are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.
Accession Number:
edsbas.286279DC
Database:
BASE

Weitere Informationen

Emails are essential in present century communication however spam emails have contributed negatively to the success of such communication. Studies have been conducted to classify messages in an effort to distinguish between ham and spam email by building an efficient and sensitive classification model with high accuracy and low false positive rate. Regular rule-based classifiers have been overwhelmed and less effective by the geometric growth in spam messages, hence the need to develop a more reliable and robust model. Classification methods employed includes SVM (support vector machine), Bayesian, Naïve Bayes, Bayesian with Adaboost, Naïve Bayes with Adaboost. However, for this project, the Bayesian was employed using Python programming language to develop a classification model. Keywords: machine learning (ML), machine learning classifier, Naïve Bayes, SVM, Adaboost, spam classification, ham. DOI:10.7176/CEIS/11-3-04 Publication date:May 31st 2020