基于HHO-KNN优化算法的垃圾邮件检测研究  

Spam Detection Based on HHO-KNN Optimization Algorithm

作　　者：陈亮[1] 朱元凯[1] 李长英[1] CHEN Liang;ZHU Yuan-kai;LI Chang-ying(Taishan Vocational and Technical College,Taian 271000,Shandong)

机构地区：[1]泰山职业技术学院,山东泰安271000

出　　处：《电脑与电信》2022年第9期73-77,共5页Computer & Telecommunication

摘　　要：垃圾邮件是一直困扰人们的问题。随着网络用户和网络企业的增长,垃圾邮件的数量也在增长。垃圾邮件除广告外,还经常带有不健康的内容。在垃圾邮件分析中,将哈里斯鹰优化算法与K-最近邻算法相结合,提出了一种新的垃圾邮件分类算法HHO-KNN优化算法;并将本文算法与其他算法进行了比较。实验结果表明,本文算法在分类精度方面优于其他算法,准确率达到94.3%。Spam is the problem that has been puzzling people.With the growth of network users and network enterprises,the number of spam is also growing.In addition to advertising,spam often does some illegal activities.In this paper,Harris Hawks Optimizer al-gorithm and K-Nearest Neighbor algorithm are combined to propose a new spam classification algorithm.The algorithm in this pa-per is compared with other algorithms.Experimental results show that this algorithm is superior to other algorithms in classification accuracy,and the accuracy rate is 94.3%.

关 键 词：哈里斯鹰优化算法 K-最近邻算法 垃圾邮件 机器学习 

分 类 号：TP393.098[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]