光纤扰动信号识别的可解释性特征选择方法  

Interpretable Feature Selection Method for Optical-Fiber Disturbance Signal Recognition

作　　者：孙敏 方捻 Sun Min;Fang Nian(School of Communication and Information Engineering,Key Laboratory of Specialty Fiber Optics and Optical Access Networks,Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学通信与信息工程学院,特种光纤与光接入网重点实验室,特种光纤与先进通信国际合作联合实验室,上海200444

出　　处：《光学学报》2024年第21期74-83,共10页Acta Optica Sinica

基　　金：国家自然科学基金(62075123);高等学校学科创新引智计划(111)资助(D20031)。

摘　　要：特征选择对基于机器学习方法的相敏光时域反射仪(φ-OTDR)系统的扰动信号的识别具有重要意义。提出一种高效的、具有可解释性的特征选择方法。该方法利用机器学习可解释性方法[沙普利加和解释(SHAP)]量化特征对模型的贡献,并按特征重要性进行排名,选择若干重要的特征构建特征子集。利用北京交通大学的开源数据集,提取6种扰动事件的22种特征,并构建4种常用的分类模型进行信号识别。在保证识别准确率的前提下,根据模型的特征重要性排名结果的差异性,选择不同数量的特征重新训练模型后,识别时间均有不同程度的减少,识别性能获得不同程度的提升,其中随机森林的性能最优,平均识别准确率提高到96.5%,且单个样本的平均识别时间减少19.3%。在选择同样数量的特征条件下,所提方法的平均识别准确率均高于其他方法。实验结果证实了所提可解释性特征选择方法的优越性和可靠性。Objective The distributed optical fiber sensing system based on a phase-sensitive optical time-domain reflectometer(φ-OTDR)has been widely used for disturbance signal recognition in perimeter security,pipeline monitoring,railway transportation monitoring,and other fields,due to its advantages of high sensitivity,multi-point monitoring,and wide coverage.Currently,machine learning-based methods are the primary approach to enhance the accuracy of disturbance signal recognition.Classical machine learning algorithms require preprocessing of raw input signals through manual feature extraction.Typically,increasing the number of extracted features is aimed at achieving higher recognition accuracy with the growth in the number of disturbance events.However,introducing irrelevant features can adversely affect recognition accuracy and efficiency.Therefore,the feature selection process,which eliminates irrelevant features to strengthen recognition performance,plays a crucial role in the preprocessing stage.Feature selection methods can be categorized into three types:filter,wrapper,and embedded methods.Particularly,most feature selection methods used for optical fiber disturbance signal recognition fall under the filter method category,often overlooking the relationship between features and models.In this study,we aim to develop a more efficient and interpretable feature selection method for identifying key features to further boost recognition performance.Methods We propose a novel feature selection method based on Shapley additive explanations(SHAP),which is an explainable artificial intelligence(XAI)method.SHAP is inspired by game theory to calculate the Shapley value,which can quantify the contribution of each feature to the model’s prediction(Equation 1).We use SHAP to obtain the mean SHAP value for a classification model.The higher the mean,the more important the feature.We rank the features by importance and select some of the most significant ones to form a feature subset while ensuring high recognition rates.This

关 键 词：传感器 相敏光时域反射仪 信号识别 特征选择 机器学习可解释性 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]