An improved dandelion optimizer algorithm for spam detection next-generation email filtering system

Tubishat, M, Al-Obeidat, F, Sadiq, AS ORCID: https://orcid.org/0000-0002-5746-0257 and Mirjalili, S, 2023. An improved dandelion optimizer algorithm for spam detection next-generation email filtering system. Computers, 12 (10): 196. ISSN 2073-431X

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Abstract

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve DO's ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), Chimp Optimization Algorithm (ChOA), Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO al-gorithm, which represents a promising approach for solving high-dimensional optimization prob-lems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning.

Item Type:	Journal article
Publication Title:	Computers
Creators:	Tubishat, M., Al-Obeidat, F., Sadiq, A.S. and Mirjalili, S.
Publisher:	MDPI
Date:	28 September 2023
Volume:	12
Number:	10
ISSN:	2073-431X
Identifiers:	Number Type 10.3390/computers12100196 DOI 1808376 Other
Rights:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	25 Sep 2023 15:01
Last Modified:	03 Nov 2023 14:56
URI:	https://irep.ntu.ac.uk/id/eprint/49809

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