SPAM Detection: Naïve Bayesian Classification and RPN Expression-Based LGP Approaches Compared

Citation data:

Software Engineering Perspectives and Application in Intelligent Systems, ISSN: 2194-5357, Vol: 465, Page: 399-411

Publication Year:
2016
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Repository URL:
http://publikace.k.utb.cz/handle/10563/1006428; http://hdl.handle.net/10563/1006428
DOI:
10.1007/978-3-319-33622-0_36
Author(s):
Meli, Clyde; Komínková Oplatková, Zuzana
Publisher(s):
Springer Nature; Springer Verlag
Tags:
Engineering; Computer Science; Genetic programming (GP); Linear genetic programming (LGP); Naïve bayesian classifier; Reverse polish notation (RPN); Spam detection
book chapter description
An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naïve Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters.