一种适用于航天大数据的深度决策树模型

A Deep Decision Tree Model for Aerospace Big Data

常规处理百万网格航天大数据的物理量回归分析方法不适用于复杂的流场环境,可使用多种机器学习模型解决该问题。但已有的机器学习模型无法同时具备高预测精度、模型可解释性和大数据处理能力。对此,提出了一种新型深度决策树模型。基于堆叠的深度森林模型,通过自适应多粒度扫描和自生长级联森林对隐藏特征进行提取和利用。使用航天大数据进行实验,结果表明所提模型在预测精度、泛化性能和核心功能增益等方面优于随机森林、XGBoost和LightGBM模型。

The conventional quantitative regression analysis method for processing millions of grid aerospace big data is not suitable for complex flow field environments.Under this background,machine learning models can be used to solve this problem.However,existing machine learning models cannot have sufficient prediction accuracy,model interpretability,and big data processing capabilities at the same time.In this regard,a new deep decision tree model is proposed.Based on the stacked deep forest model,the hidden features are extracted and utilized by adaptive multi⁃granularity scanning and self⁃growing cascade forests.Using aerospace big data for experiments,the results show that the proposed model is superior to the random forest,extreme gradient boosting and light gradient boosting machine models in terms of prediction accuracy,generalization performance and core function gain.