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Vol.9, No.1, February 2020. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Polynomial Neural Networks Versus Other Spam Email Filters: An Empirical Study
Mayy Al-Tahrawi, Mosleh Abualhaj, Sumaya Al-Khatib
© 2020 Mayy Al‐Tahrawi, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 9, Issue 1, Pages 136‐143, ISSN 2217-8309, DOI: 10.18421/TEM91-19, February 2020.
Received: 31 October 2019. Revised: 24 January 2020.
Abstract:
Spam or junk e-mail problems are increasing exponentially due to the huge growth of internet users and their high dependency on e-mails as the main communication means nowadays. Such problems result in huge amounts of time and cost waste for both individuals and organizations. This research paper directly compares the performance of four famous text classification algorithms in classifying emails and detecting the spam ones: Polynomial Neural Networks (PNNs), the k-nearest neighbour (k-NN), Support Vector Machines (SVM) and Naïve Bayes (NB). Results of the experiments conducted on Lingspam, the benchmark E-mail corpus, in this research work reveals that PNNs is a competitive spam filter to the sate-of-the-art spam filters. It recorded either equal or superior results in most of the performance measures used to evaluate the four spam filters.
Keywords –Spam e-mail filtering, Polynomial Neural Networks, k-Nearest Neighbour, Support Vector Machines, Naïve Bayes. |
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