利用GMSMFO增强特征选择:一种用于入侵检测的机器学习全局优化算法

IF 4.8 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nazar K. Hussein, Mohammed Qaraad, Souad Amjad, M. Farag, Saima Hassan, S. Mirjalili, Mostafa A. Elhosseini
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引用次数: 0

摘要

本文讨论了飞蛾-火焰优化算法(MFO)的局限性,该算法是一种用于求解优化问题的元启发式算法。该算法利用飞蛾的横向定向导航技术,对这类问题进行求解。然而,MFO的性能依赖于火焰产生和螺旋搜索组件,在火焰多样性和飞蛾寻找解决方案的能力方面,搜索机制仍有待改进。作者提出了GMSMFO的修正版本,该版本使用了一种新的高斯突变机制,并缩小了MFO,以增强种群多样性并平衡探索和开发能力。该研究使用CEC 2017基准和20个数据集(包括高维入侵检测系统数据集)评估了GMSMFO的性能。将该算法与其他先进的元启发式算法进行了比较,并使用Friedman和Wilcoxon秩和等统计检验对其性能进行了评估。研究表明,GMSMFO具有很强的竞争力,并且经常优于其他算法。它可以识别出理想的特征子集,提高分类精度,减少使用的特征数量。本文的主要贡献在于改善了勘探/开采平衡,扩大了局部搜索范围。测距控制器和高斯突变增强了系统的导航性和多样性。在29个基准上比较了GMSMFO算法与传统和先进的元启发式算法,并将其应用于包括入侵检测系统在内的20个基准上的二值特征选择。统计检验(Wilcoxon秩和和Friedman)评估了GMSMFO与其他算法的性能。该算法的源代码可从https://github.com/MohammedQaraad/GMSMFO-algorithm获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection
The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search mechanism could still be improved concerning the diversity of flames and the moths' ability to find solutions. The authors propose a revised version called GMSMFO, which uses a Novel Gaussian mutation mechanism and shrink MFO to enhance population diversity and balance exploration and exploitation capabilities. The study evaluates the performance of GMSMFO using the CEC 2017 benchmark and 20 datasets, including a high-dimensional intrusion detection system dataset. The proposed algorithm is compared to other advanced metaheuristics, and its performance is evaluated using statistical tests such as Friedman and Wilcoxon rank-sum. The study shows that GMSMFO is highly competitive and frequently superior to other algorithms. It can identify the ideal feature subset, improving classification accuracy and reducing the number of features used. The main contribution of this research paper includes the improvement of the exploration/exploitation balance and the expansion of the local search. The ranging controller and Gaussian mutation enhance navigation and diversity. The research paper compares GMSMFO with traditional and advanced metaheuristic algorithms on 29 benchmarks and its application to binary feature selection on 20 benchmarks, including intrusion detection systems. The statistical tests (Wilcoxon rank-sum and Friedman) evaluate the performance of GMSMFO compared to other algorithms. The algorithm source code is available at https://github.com/MohammedQaraad/GMSMFO-algorithm.
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来源期刊
Journal of Computational Design and Engineering
Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction
CiteScore
7.70
自引率
20.40%
发文量
125
期刊介绍: Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: • Theory and its progress in computational advancement for design and engineering • Development of computational framework to support large scale design and engineering • Interaction issues among human, designed artifacts, and systems • Knowledge-intensive technologies for intelligent and sustainable systems • Emerging technology and convergence of technology fields presented with convincing design examples • Educational issues for academia, practitioners, and future generation • Proposal on new research directions as well as survey and retrospectives on mature field.
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