Feature Selection Using Hybrid Metaheuristic Algorithm for Email Spam Detection

In the present study, Krill Herd (KH) is proposed as a Feature Selection tool to detect spam email problems. This works by assessing the accuracy and performance of classifiers and minimizing the number of features. Krill Herd is a relatively new technique based on the herding behavior of small crus...

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Bibliographic Details
Published in:	Cybernetics and information technologies : CIT Vol. 24; no. 2; pp. 156 - 171
Main Authors:	Al-Rawashdeh, Ghada Hammad, Khashan, Osama A, Al-Rawashde, Jawad, Al-Gasawneh, Jassim Ahmad, Alsokkar, Abdullah, Alshinwa, Mohammad
Format:	Journal Article
Language:	English
Published:	Sciendo 01-06-2024
Subjects:	Feature selection Hybridization Krill Herd Algorithm Metaheuristic Algorithm Tabu search
Online Access:	Get full text
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Summary:	In the present study, Krill Herd (KH) is proposed as a Feature Selection tool to detect spam email problems. This works by assessing the accuracy and performance of classifiers and minimizing the number of features. Krill Herd is a relatively new technique based on the herding behavior of small crustaceans called krill. This technique has been combined with a local search algorithm called Tabu Search (TS) and has been successfully employed to identify spam emails. This method has also generated much better results than other hybrid algorithm optimization systems such as the hybrid Water Cycle Algorithm with Simulated Annealing (WCASA). To assess the effectiveness of KH algorithms, SVM classifiers, and seven benchmark email datasets were used. The findings indicate that KHTS is much more accurate in detecting spam mail (97.8%) than WCASA.
ISSN:	1314-4081 1314-4081
DOI:	10.2478/cait-2024-0021