<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Feature Selection Using Hybrid Metaheuristic Algorithm for Email Spam Detection

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"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...

Feature Selection Using Hybrid Metaheuristic Algorithm for Email Spam Detection

Publié en ligne: 27 juin 2024

Pages: 156 - 171

Reçu: 13 nov. 2023

Accepté: 04 avr. 2024

DOI: https://doi.org/10.2478/cait-2024-0021

Mots clés
Krill Herd Algorithm, Tabu search, Metaheuristic Algorithm, Feature selection, Hybridization

© 2024 Ghada Hammad Al-Rawashdeh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Feature Selection Using Hybrid Metaheuristic Algorithm for Email Spam Detection

Publié en ligne: 27 juin 2024

Pages: 156 - 171

Reçu: 13 nov. 2023

Accepté: 04 avr. 2024

DOI: https://doi.org/10.2478/cait-2024-0021

Mots clésKrill Herd Algorithm, Tabu search, Metaheuristic Algorithm, Feature selection, Hybridization

© 2024 Ghada Hammad Al-Rawashdeh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Krill Herd Algorithm, Tabu search, Metaheuristic Algorithm, Feature selection, Hybridization