首页|基于混合式特征选择的辐射源个体识别

基于混合式特征选择的辐射源个体识别

扫码查看
原文链接
  • 万方数据
  • 维普
为提高辐射源个体识别的准确率和运算效率,提出了一种基于混合式特征选择的辐射源个体识别.封装式特征选择方法分类正确率高,但计算复杂度高,处理高维数据时效率低.嵌入式特征选择方法计算复杂度低,但依赖于特定分类器.针对上述问题,综合封装式和嵌入式特征选择方法的特点,首先对信号数据使用3种嵌入式方法(随机森林、XGBoost和Ligh-tGBM)初选特征,分别得到随机森林子集、XGBoost子集和LightGBM子集.然后使用封装式方法对初选后得到的子集进行第二次降维,其中搜索策略分别使用序列后向搜索策略和蚁群优化算法,分类算法使用LightGBM.混合式方法共得到6种特征选择模型,通过对比各个模型得到的分类正确率和最优子集中的特征个数,确定最佳混合式特征选择模型.
Specific Emitter Identification Based on Hybrid Feature Selection
To improve the accuracy and computational efficiency of specific emitter identification,a specific emitter identification based on hybrid feature selection is proposed.Wrapped feature selection methods have high classification accuracy,but it has high computational complexity and low efficiency in processing high-dimensional data.Embedded feature selection methods have low computational complexity,but rely on specific classifiers.To address the above problems,combining the characteristics of wrapped and embedded feature selection methods,firstly,three embedded methods(Random Forest,XGBoost,and LightGBM)are used to initially select features for signal data,and a random forest subset,an XGBoost subset and a LightGBM subset are ob-tained respectively.Secondly,the wrapped methods are used to perform a second dimensionality reduction on the subset obtained after the primary selection.Sequential backward selection and an ant colony optimization algorithm are used as research strategies respectively,while LightGBM is used as the classification algorithm.A total of six feature selection models are obtained from the proposed hybrid feature selection method.The optimal hybrid feature selection model is determined by comparing the classifica-tion accuracy and the number of features in the optimal subset obtained by each model.

Specific emitter identificationFeature selectionRandom forestXGBoostLightGBMSequential backward selectionAnt colony optimization

顾楚梅、曹建军、王保卫、徐雨芯

展开 >

国防科技大学第六十三研究所 南京 210007

国防科技大学大数据与决策实验室 长沙 410073

南京信息工程大学计算机学院网络空间安全学院 南京 210044

辐射源个体识别 特征选择 随机森林 XGBoost LightGBM 序列后向搜索策略 蚁群优化

国家自然科学基金国家自然科学基金中国博士后科学基金中国博士后科学基金

719012156137119620090461425201003797

2024

10.11896/jsjkx.230300216

计算机科学
重庆西南信息有限公司（原科技部西南信息中心）

计算机科学

CSTPCD北大核心
影响因子：0.944
ISSN：1002-137X
年,卷(期)：2024.51(5)
  • 29