Safety Assessment Method Based on Degree of Feature Matching and Fusion of Heterogeneous Sub-Models
The quality of machine learning models affects the prediction accuracy as well as the fit between the input and output results.In complex systems,when a single model is used to evaluate system security problems,the results are easily affected by data volume,data format,model structure,environmental interference,and other factors.These issues reduce the effectiveness of the model in simultaneously solving multiple problems,although being effective in solving one problem at a time,leading to unsatisfactory results.To address these issues,this study proposes a safety assessment method based on degree of feature matching and heterogeneous sub-model fusion.First,the datasets are divided into different sizes based on the output values of the sampled data to construct sub-models.Second,the weights of each sub-model are obtained by calculating the matching degree of each new data sample with these sub-models.Finally,the final multi-model fusion result is obtained by fusing the sub-outputs of all the sub-models based on their respective weights.The proposed safety assessment method is applied to the mining dataset of the Xiaoyun Coal Mine in Jining City,Shandong Province.The experimental results show that when the sub-model is constructed at a ratio of 330/70,the Root Mean Square Error(RMSE)of this proposed method is reduced by 15.13％,51.67％,and 12.46％compared with the diversified single model,small-sample single model,and traditional multi-model,respectively.The proposed method fully integrates the effective information provided by each sub-model,reduces and disperses the prediction error of a single model,and improves the prediction accuracy and generalization ability of the model.
degree of feature matchingheterogeneous sub-modelssingle modelmulti-model fusionsafety assessment
万方数据
维普
