Feature Selection Using Hybrid Metaheuristic Algorithm for Email Spam Detection

Abstract

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.