A parameter selection optimization method, system and equipment in random forest model training

Abstract

The invention belongs to the technical field of model optimization, and discloses a parameter selection optimization method, equipment and terminal in random forest model training, and the parameter selection optimization method in random forest model training comprises the steps of determining the parameter influence of a random forest; building a parameter optimization algorithm based on QGA-RF; and performing random forest optimization based on the quantum genetic algorithm. Experiments prove that through QGA optimization, the classification performance of the random forest algorithm is improved, and the training time of the model is within an acceptable range; compared with the GA, the QGA has better global search capability and is not easy to fall into a local optimal solution. Meanwhile, an improved QGA is used for optimizing the random forest classification model, the influence of two parameters in the random forest on the model classification performance is given, a pair of optimal parameter solutions are searched through the QGA, and finally the effectiveness of the method is proved through experiments.

Description

technical field [0001] The invention belongs to the technical field of model optimization, and in particular relates to an optimization method, equipment and terminal for parameter selection in random forest model training. Background technique [0002] At present, compared with some basic learning algorithms in machine learning, the random forest algorithm has been proved to have a higher classification accuracy, and it also shows better classification performance in nonlinear classification problems. Therefore, the random forest algorithm is often used by many researchers at home and abroad in classification prediction problems. Zhou et al. used a selective integration algorithm to combine multiple decision trees to build a random forest model, and eliminated decision trees with low predictive performance to improve the accuracy of the model. El-Adawi R and others proposed a statistical blockade method, using support vector machines and random forests to identify and clas...

AI Technical Summary

Problems solved by technology

[0007] (1) The existing technical solutions for the random forest algorithm have not carried out research on optimization algorithms and other aspects. In the implementation of quantum genetic algorithms, depending on the optimization goals, the realization efficiency may be different

[0008] (2) The single random forest algorithm will have overfitting phenomenon and the classification of minority classes is not accurate enough. At the same time, it is not easy to achieve a balance between accuracy and computational overhead, which requires continuous optimization and improvement.

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