基于改进鲸鱼算法优化svm参数和特征选择 郭辉.pdf

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吉林大学学报 出一种改进鲸鱼优化算法同步优化 SVM 的特征选择模型。首先利用 Levy 飞行策略对鲸鱼优化算法的螺旋更新位
置进行变异扰动，利用单纯形策略中的反射操作对种群中的精英个体进行反射点求解的改进，标准函数的测试结
果证明其改进能有效提高算法的收敛速度和计算精度；其次将 SVM 核参数和特征选择目标作为共同优化对象，在
获得最优核参数的同时得到相对应的最优特征子集；最后对 UCI 标准数据集和真实乳腺癌数据集进行特征选择仿
真实验，在平均分类准确率、平均适应度值、适应度标准差和所选特征个数上进行评价。结果表明，本文所提算
法在降低特征维度，实现数据分类上效果明显。在真实乳腺癌数据集上的分类精度与传统支持向量机相比提高了
%。
关键词：鲸鱼优化算法；特征选择；单纯形；Levy 飞行；SVM；数据分类
中图分类号： 文献标志码：A
DOI：.jdxbgxb20211348
SVM parameters and feature selection were optimized based on
improved whale algorithm
GUO Hui1,2, FU Jie-di1,2, LI Zhen-dong1,2, YAN Yan3, LI Xiao1,2
(1. School of Information Engineering, Ningxia University, Yinchuan 750021, China; 2. Collaborative Innovation Center for Ningxia
Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Yinchuan 750021, China; 3.
Electric Power Research Institute, State Grid Ningxia Power Co., Ltd., Yinchuan 750011, China)
Abstract: Prevalent scenario of Support Vector Machine (SVM) in data classification exists the low recognition accuracy problems,
this paper proposes a feature selection model for synchronous optimization of SVM with improved Whale optimization algorithm.
Firstly, the Levy flight strategy was used to perturb the spiral update position of the whale optimization algorithm and the reflection
operation of the simplex method to improve the reflection point solution of the