<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

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

Online veröffentlicht: 09. Okt. 2023

Seitenbereich: -

Eingereicht: 27. Nov. 2022

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00554

Schlüsselwörter
Nonlinear two-level programming, Decision tree model, Classification accuracy, CART decision tree, Convergence

© 2023 Jinshan Lin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Online veröffentlicht: 09. Okt. 2023

Seitenbereich: -

Eingereicht: 27. Nov. 2022

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00554

SchlüsselwörterNonlinear two-level programming, Decision tree model, Classification accuracy, CART decision tree, Convergence

© 2023 Jinshan Lin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Nonlinear two-level programming, Decision tree model, Classification accuracy, CART decision tree, Convergence