A study of algorithms for solving nonlinear two-level programming problems oriented to decision tree models

Author:

Lin Jinshan1,Lin Min1,Xu Hang1

Affiliation:

1. 1 College of Electronic & Information Engineering , Putian University , Putian , Fujian , , China .

Abstract

Abstract In this paper, the original two-level planning problem is transformed into a single-level optimization problem by combining the penalty function method for the large amount of data processing involved in the training process of the decision tree model, setting the output as a classification tree in the iterative process of the CART decision tree, and recursively building the CART classification tree with the training set to find the optimal solution set for the nonlinear two-level planning problem. It is verified that the proposed solution method is also stable at a convergence index of 1.0 with a maximum accuracy of 95.37%, which can provide an efficient solution method for nonlinear two-level programming problems oriented to decision tree models.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Temperature Prediction for Stored Grain: A Multi- model Fusion Approach Based on Machine Learning;2024 9th International Conference on Electronic Technology and Information Science (ICETIS);2024-05-17

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