摘要
针对雷达辐射源信号识别课题中复杂的参数配置问题,从机器学习参数优化的研究入手,提出了一种基于树结构的机器学习流程优化方法。该方法利用遗传编程生成基于树结构的机器学习流程,并对其结构和参数进行进化,得到表现最佳的带参数的机器学习流程。该流程可以包括特征处理和建模的任意组合,实现对原始数据集的学习和识别。与人工参数配制的一对一支持向量机在两种不同维度的雷达信号特征集上进行对比识别,相比之下,该方法无须繁琐的参数配置,准确率提高超过6%,证明该方法得到的基于树结构的机器学习流程有着明显的优势。
In order to solve the problem of complex parameter configuration in radar emitter signal recognition,this paper found a tree-based machine learning pipeline optimization method,based on the study of machine learning parameter optimization. This method used GP to generate the tree-based machine learning pipeline,and got the best machine learning pipeline with the best parameters through the evolution of the pipeline and parameters. The pipeline could include any combination of feature processing and modeling to achieve the learning and recognition of the original data set. This paper compared recognition effect of this method and the SVM method in the radar emitter signal feature sets of two different dimensions. In contrast,this method does not require cumbersome parameter configuration,and the highest accuracy is improved by more than 6%. It is proved that the tree-based machine learning pipeline obtained by this method has obvious advantages.
作者
涂同珩
金炜东
Tu Tongheng;Jin Weidong(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《计算机应用研究》
CSCD
北大核心
2019年第1期191-193,共3页
Application Research of Computers
基金
国家自然科学基金重点资助项目(61134002)
中央高校基本科研业务费专项资金资助项目(SWJT12CX038U)
关键词
自动机器学习
超参数优化
遗传编程
雷达辐射源信号
支持向量机
automatic machine learning
hyperparameter optimization
genetic programming
radar emitter signal
SVM
