摘要
支持向量机是检测异常点的常用方法,但其仍然存在难以高效获取最优参数,导致检测效率低、稳定性差的问题。鉴于此,提出一种改进的麻雀搜索算法ISSA,并将其用于优化支持向量机参数。首先,采用改进折射反向学习和可变对数螺线改进传统麻雀搜索算法;然后,利用改进麻雀搜索算法ISSA对支持向量机参数进行优化;最后,将优化后的支持向量机用于异常点检测。仿真实验结果表明,在G-mean和F-measure 2个评价指标上,利用ISSA优化后的支持向量机检测效果明显优于其它3种分类算法,具有更优秀的检测效率、稳定性和泛化能力。
Support vector machine(SVM)is a common method for outlier detection.However,there are still common problems that it is difficult to quickly and effectively obtain the optimal paramet-ers,resulting in low detection efficiency and poor stability.In view of this,an improved sparrow search algorithm is proposed to optimize the parameters of SVM.Firstly,the traditional sparrow search algorithm is improved by improved refraction reverse learning and variable logarithm spiral.Then,the improved sparrow search algorithm(ISSA)is used to optimize the parameters of SVM.Finally,the optimized SVM is used in the field of outlier detection.The simulation results show that,under the two evaluation indexes of G-mean and F-measure,the optimized SVM is obviously better than the other three classification algorithms,and has better detection efficiency,stability,and generalization ability.
作者
唐宇
代琪
杨梦园
陈丽芳
TANG Yu;DAI Qi;YANG Meng-yuan;CHEN Li-fang(College of Science,North China University of Science and Technology,Tangshan 063210;Department of Automation,China University of Petroleum(Beijing),Beijing 102249;Key Laboratory of Data Science and Application of Hebei Province,Tangshan 063210,China)
出处
《计算机工程与科学》
CSCD
北大核心
2023年第2期346-354,共9页
Computer Engineering & Science
基金
国家自然科学基金(52074126)。
关键词
麻雀搜索算法
支持向量机
折射反向学习
可变对数螺线
异常点检测
sparrow search algorithm
support vector machine
refraction reverse learning
variable logarithmic spiral
outlier detection
