RANDOM FOREST INTEGRATION METHOD BASED ON FEATURE MAPPING LAYER AND ENHANCEMENT LAYER STRUCTURES

摘要

Disclosed in the present invention is a random forest integration method based on feature mapping layer and enhancement layer structures, applicable to the field of machine learning. The method mainly comprises two parts: model design and model training. The model design part mainly comprises two parts: the design of a feature mapping layer and an enhancement layer, and the design of an output weight; a neural network node consisting of a random forest and a complete random forest is designed so as to adaptively adjust the width of a model; a local weight is obtained by means of the average accuracy of the nodes, and the output weight is calculated; and finally a final output vector is solved. The method is high in automation degree, adaptively decides the size of the model by means of the training, is easy for theoretical analysis, is good in interpretability and is strong in parallelization capability.