Many real-world datasets suffer from the unavoidable issue of missing values,and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large err...Many real-world datasets suffer from the unavoidable issue of missing values,and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large errors.In this paper,we propose a random subspace sampling method,RSS,by sampling missing items from the corresponding feature histogram distributions in random subspaces,which is effective and efficient at different levels of missing data.Unlike most established approaches,RSS does not train on fixed imputed datasets.Instead,we design a dynamic training strategy where the filled values change dynamically by resampling during training.Moreover,thanks to the sampling strategy,we design an ensemble testing strategy where we combine the results of multiple runs of a single model,which is more efficient and resource-saving than previous ensemble methods.Finally,we combine these two strategies with the random subspace method,which makes our estimations more robust and accurate.The effectiveness of the proposed RSS method is well validated by experimental studies.展开更多
In this paper,we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree(REPT)as a base classifier with the Bagging(B),Decorate(D),and Random Subspace(RSS)ensemble learning te...In this paper,we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree(REPT)as a base classifier with the Bagging(B),Decorate(D),and Random Subspace(RSS)ensemble learning techniques for spatial prediction of rainfallinduced landslides in the Uttarkashi district,located in the Himalayan range,India.To do so,a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets.Root Mean Square Error(RMSE)and Area Under the receiver operating characteristic Curve(AUC)were used to evaluate the training and validation performances of the models.The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides.The D-REPT model with RMSE=0.351 and AUC=0.907 was identified as the most accurate model,followed by RSS-REPT(RMSE=0.353 and AUC=0.898),B-REPT(RMSE=0.396 and AUC=0.876),and the single REPT model(RMSE=0.398 and AUC=0.836),respectively.The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61772256 and 61921006.
文摘Many real-world datasets suffer from the unavoidable issue of missing values,and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large errors.In this paper,we propose a random subspace sampling method,RSS,by sampling missing items from the corresponding feature histogram distributions in random subspaces,which is effective and efficient at different levels of missing data.Unlike most established approaches,RSS does not train on fixed imputed datasets.Instead,we design a dynamic training strategy where the filled values change dynamically by resampling during training.Moreover,thanks to the sampling strategy,we design an ensemble testing strategy where we combine the results of multiple runs of a single model,which is more efficient and resource-saving than previous ensemble methods.Finally,we combine these two strategies with the random subspace method,which makes our estimations more robust and accurate.The effectiveness of the proposed RSS method is well validated by experimental studies.
文摘In this paper,we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree(REPT)as a base classifier with the Bagging(B),Decorate(D),and Random Subspace(RSS)ensemble learning techniques for spatial prediction of rainfallinduced landslides in the Uttarkashi district,located in the Himalayan range,India.To do so,a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets.Root Mean Square Error(RMSE)and Area Under the receiver operating characteristic Curve(AUC)were used to evaluate the training and validation performances of the models.The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides.The D-REPT model with RMSE=0.351 and AUC=0.907 was identified as the most accurate model,followed by RSS-REPT(RMSE=0.353 and AUC=0.898),B-REPT(RMSE=0.396 and AUC=0.876),and the single REPT model(RMSE=0.398 and AUC=0.836),respectively.The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.
