学校代码10530学号0181511188分类号TP181密级硕硕士士学学位位论论文于基于TPE-LightGBM算法和和SHAP值的信贷违约预测学学位位申申请请人孔令莹指导教师尹福其副教授学院名称数学与计算科学学院学科专业应用统计研究方向应用统计二〇二一年四月八日
(least absolute shrinkage and selection operator, LASSO)算法选择用于变压器故障诊断的有效特征量;其次,构建基于LightGBM的变压器故障诊断方法,并引入TPE算法对LightGBM诊断模型参数进行优化,形成最优故障诊断模型;最后,选用精确度,召回率和F1分数等评价指标对所提诊断模型性能进行评估.研究结果表明,TPE-LightGBM的平均准确...
First, the production data is preprocessed, and the industrial parameters are fitted using the LightGBM prediction model. Then, to further increase the model's prediction accuracy, the TPE optimization method is used to optimize the LightGBM hyperparameters. Finally, the optimization of current ...
Using LightGBM-TPE to calculate the SHAP value of each feature, we find that “Longitude”, “Latitude”, “Hour” and “Day_of_Week” are four risk factors most closely related with accident severity. Visualization for the data further verifies this conclusion. Overall, our research tries to...
To address these challenges, this study proposes a novel network intrusion detection method based on an improved binary simulated annealing algorithm (IBSA) and TPE-FL-LightGBM. First, by integrating Focal Loss into the loss function of the LightGBM classifier, we introduce cost-sensitive learning, ...