Random Forest Improvement Method Based on Feature Importance

Decision tree-based random forest algorithms can efficiently process multi-source heterogeneous data, accurately predict complex hydrological processes, and optimize power plant operation decisions. At the same time, it has the ability to resist noise interference and analyze feature importance, providing intelligent management of water conservancy and hydropower systems. However, when the proportion of highly informative features in a dataset is low, the performance of traditional random forests drops significantly. To overcome this problem, this paper propose a random forest improvement method based on feature importance. In this method, we first screen out the important features and calculate the importance weights, then construct a certain number of random subspaces based on the weights, and finally dynamically adjust the weights and construct new random subspaces based on the sliding window. The experimental results show that the model proposed in this paper is better than the traditional random forest model.