吉林大学学报（理学版）2025，Vol.63Issue(3)：861-866,6.DOI:10.13413/j.cnki.jdxblxb.2024003
改进SHO算法优化随机森林模型
Improve SHO Algorithm to Optimize Random Forest Model
摘要
Abstract
Aiming at the problem of low-quality initial solutions and insufficient diversity,we proposed a random forest model that introduced the Logistic chaos mapping to improve the optimization of the sea horse optimization algorithm.Firstly,after improving the sea horse optimization algorithm,it was combined with the random forest algorithm to improve the discriminative accuracy of the classic random forest algorithm.Secondly,in order to verify the performance of new model,comparative experiment was conducted by using five models for four evaluation metrics.The experimental results show that the model has accuracy rate of 96.15％,precision of 100％,recall rate of 92.31％,and F1-Score of 96.00％,which improves the performance of the random forest method.关键词
优化算法/海马优化算法/随机森林/分类算法/参数优化Key words
optimization algorithm/sea horse optimization algorithm/random forest/classification algorithm/parameter optimization分类
信息技术与安全科学引用本文复制引用
付海涛,张智勇,王增辉,金晨磊..改进SHO算法优化随机森林模型[J].吉林大学学报（理学版）,2025,63(3):861-866,6.基金项目
吉林省教育厅科学技术研究项目(批准号:JJKH20250567KJ). （批准号:JJKH20250567KJ）
