面向单脉冲信号分类的集成特征选择与评价  

Ensemble Feature Selection Method for Single Pulse Classification

作　　者：张金区[1] 凌毓 杜平 李乡儒 李慧[1] ZHANG Jin-qu;LING Yu;DU Ping;LI Xiang-ru;LI Hui(School of Computer Science,South China Normal University,Guangzhou 510631;School of Building Information,Guangdong Construction Vocational Technology Institute,Qingyuan 511500)

机构地区：[1]华南师范大学计算机学院,广州510631 [2]广东建设职业技术学院建筑信息学院,清远511500

出　　处：《天文学报》2023年第5期57-67,共11页Acta Astronomica Sinica

基　　金：国家自然科学基金项目(11973022、61273248、61075033);校一般科研项目(KY2021-36)资助。

摘　　要：受大量射频干扰信号影响,快速从海量观测数据中准确识别出单脉冲信号已成为天文数据处理的一项重要任务,而设计和提取有效数据特征,是利用机器学习进行单脉冲信号高效识别的决定因素.针对如何选择最优特征,进而提升单脉冲信号的分类精度这一关键问题,设计了面向单脉冲信号分类的集成特征选择方法.方法首先混合单脉冲信号的参数特征、统计特征和抽象特征,然后分别利用5种单一特征选择方法选出各自的最优特征集,最后利用贪心策略对5种单一方法获取的最优特征集进行集成筛选,获取最优集成特征集.实验表明,最优特征集合既包含统计特征也包含抽象特征.在相同特征数量下,利用集成特征选择比单一特征选择能获得更高的模型精度,可使F1值最高提升1.8%.在海量数据背景下,集成特征选择对减少特征数量、提升分类性能和加快数据处理速度具有重要作用.features of single pulse signals,and then used five independent feature selection methods to select the corresponding optimal feature set,respectively.At last,the features selected by the five independent methods are mixed and the greedy strategy was used to select the optimal ensemble feature set.The experimental results show that the ensemble feature set can improve F1-score by value of 1.8% at most and can obtain higher accuracy than the features selected by independent methods.Under the background of high-speed and large-scale sky survey,the ensemble feature selection method plays an important role in reducing the number of features,improving classification performance and speeding up data processing.

关 键 词：脉冲信号 射电脉冲星 作用变量 方法:数据分析 

分 类 号：P161[天文地球—天文学]