Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squ...Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm.展开更多
The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we e...The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.展开更多
Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive cal...Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to timc series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM（75 1 600 ms）, that of the proposed method in different time windows is 40-60 ms, proposed method is above 0.8. So the improved method is online prediction. and the prediction accuracy（normalized root mean squared error） of the better than the traditional LS-SVM and more suitable for time series online prediction.展开更多
Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIM...Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIMIC-Ⅳ database.Clinical features were generated and selected by mutual information and grid search.Logistic regression,Random forest,LightGBM,XGBoost,and other machine learning models were constructed to predict the mortality probability.Five measurements including accuracy,precision,recall,F1 score,and area under curve(AUC) were acquired for model evaluation.An external validation was implemented to avoid conclusion bias.Results LightGBM outperformed other methods,achieving the highest AUC(0.900),accuracy(0.808),and precision(0.559).All machine learning models performed better than SAPSⅡ score(AUC=0.748).LightGBM achieved 0.883 in AUC in the external data validation.Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS Ⅱ score.展开更多
文摘Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm.
文摘The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.
基金Project (SGKJ[200301-16]) supported by the State Grid Cooperation of China
文摘Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to timc series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM（75 1 600 ms）, that of the proposed method in different time windows is 40-60 ms, proposed method is above 0.8. So the improved method is online prediction. and the prediction accuracy（normalized root mean squared error） of the better than the traditional LS-SVM and more suitable for time series online prediction.
文摘Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIMIC-Ⅳ database.Clinical features were generated and selected by mutual information and grid search.Logistic regression,Random forest,LightGBM,XGBoost,and other machine learning models were constructed to predict the mortality probability.Five measurements including accuracy,precision,recall,F1 score,and area under curve(AUC) were acquired for model evaluation.An external validation was implemented to avoid conclusion bias.Results LightGBM outperformed other methods,achieving the highest AUC(0.900),accuracy(0.808),and precision(0.559).All machine learning models performed better than SAPSⅡ score(AUC=0.748).LightGBM achieved 0.883 in AUC in the external data validation.Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS Ⅱ score.
