Shufang Song1,*, Ruyang He1, Zhaoyin Shi1, Weiya Zhang2
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 65-85, 2021, DOI:10.32604/cmes.2021.015378
Abstract The variable importance measure (VIM) can be implemented to rank or select important variables, which can effectively reduce the variable dimension and shorten the computational time. Random forest (RF) is an ensemble learning method by constructing multiple decision trees. In order to improve the prediction accuracy of random forest, advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees. Referring to the Mean Decrease Accuracy (MDA) index based on Out-of-Bag (OOB) data, the single variable, group variables and correlated variables importance measures are proposed to establish a complete VIM system… More >
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