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  • 标题:Polynomial Neural Networks Versus Other Spam Email Filters:An Empirical Study
  • 本地全文:下载
  • 作者:Mayy Al-Tahrawi ; Mosleh Abualhaj ; Sumaya Al-Khatib
  • 期刊名称:TEM Journal
  • 印刷版ISSN:2217-8309
  • 电子版ISSN:2217-8333
  • 出版年度:2020
  • 卷号:9
  • 期号:1
  • 页码:136-143
  • DOI:10.18421/TEM91‐19
  • 语种:English
  • 出版社:UIKTEN
  • 摘要: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.
  • 关键词:Spam e-mail filtering;Polynomial Neural Networks;k-Nearest Neighbour;Support Vector Machines;Naïve Bayes.
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