基于梯度提升决策树特征组合的链路识别方法 (2024)
本发明公开了一种基于梯度提升决策树特征组合的链路识别方法，包括以下步骤：S1、构建链路特征数据库并进行预处理，得到训练数据集；S2、构建基于梯度提升决策树的链路识别模型，利用训练数据集训练模型；S3、基于组合特征添加对应的辅助特征以得到准确的识别结果：基于链路识别模型获得数据-结果的可解释组合特征，基于可解释组合特征分析对应的辅助特征，添加辅助特征进行链路识别以得到准确的识别结果。本方法解决了传统链路识别技术面对复杂网络需要硬件设备实地探测链路的问题，有效利用梯度提升决策树特征组合和深度模型学习能力，提高了链路识别效率和准确率。 The invention discloses a link identification method based on gradient boosting decision tree feature combination, and the method comprises the following steps: S1, constructing a link feature database, and carrying out the preprocessing of the database, and obtaining a training data set; s2, constructing a link identification model based on a gradient lifting decision tree, and training the model by using the training data set; and S3, adding corresponding auxiliary features based on the combined features to obtain an accurate identification result: obtaining data-result interpretable combined features based on the link identification model, analyzing the corresponding auxiliary features based on the interpretable combined features, and adding t