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

<p>The author proposes a model evaluation based on the GA-SVM model to better understand the evaluation of the company’s relationship and core competitiveness. The system index is reduced by relative gray analysis, the support vector machine model is optimized by a genetic algorithm, and the specific algorithm steps are introduced. Select models from the top 100 enterprises in China’s construction industry in 2020 published by China Construction Industry Market, using relative gray to reduce the measure, and then use the genetic algorithm support vector machine (GA-SVM) model for the training model to achieve the evaluation of the core competencies of the target construction technique business. The experimental results show that the relative error of prediction of the GA-SVM(Genetic algorithm-support vector machine) model for the evaluation of the competitive core of the business is not more than 5, which meets the should be made of accurate predictions. Therefore, choosing the Gaussian radial basis kernel function (RBF) as the kernel function of the GA-SVM intelligent evaluation model is good. It proves that the GA-SVM model can analyze the relationship between the role and the important competition.</p>
</abstract>

The author proposes a model evaluation based on the GA-SVM model to better understand the evaluation of the company’s relationship and core competitiveness. The system index is reduced by relative gray analysis, the support vector machine model is optimized by a genetic algorithm, and the specific algorithm steps are introduced. Select models from the top 100 enterprises in China’s construction industry in 2020 published by China Construction Industry Market, using relative gray to reduce the measure, and then use the genetic algorithm support vector machine (GA-SVM) model for the training model to achieve the evaluation of the core competencies of the target construction technique business. The experimental results show that the relative error of prediction of the GA-SVM(Genetic algorithm-support vector machine) model for the evaluation of the competitive core of the business is not more than 5, which meets the should be made of accurate predictions. Therefore, choosing the Gaussian radial basis kernel function (RBF) as the kernel function of the GA-SVM intelligent evaluation model is good. It proves that the GA-SVM model can analyze the relationship between the role and the important competition.

Evaluation of Corporate Social Responsibility and Core Competitiveness Based on GA-SVM Model

Applied Mathematics and Nonlinear Sciences

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

The author proposes a model evaluation based on the GA-SVM model to better understand the evaluation of the company’s relationship and core competitiveness....

Evaluation of Corporate Social Responsibility and Core Competitiveness Based on GA-SVM Model

Online veröffentlicht: 05. Juli 2023

Seitenbereich: 2949 - 2960

Eingereicht: 12. Sept. 2022

Akzeptiert: 11. Jan. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00011

Schlüsselwörter
Core competitiveness, Social responsibility, Genetic algorithm, Support Vector Machines

© 2023 Yongping Guo et al., published by Sciendo

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

Evaluation of Corporate Social Responsibility and Core Competitiveness Based on GA-SVM Model

Online veröffentlicht: 05. Juli 2023

Seitenbereich: 2949 - 2960

Eingereicht: 12. Sept. 2022

Akzeptiert: 11. Jan. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00011

SchlüsselwörterCore competitiveness, Social responsibility, Genetic algorithm, Support Vector Machines

© 2023 Yongping Guo et al., published by Sciendo

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

Schlüsselwörter
Core competitiveness, Social responsibility, Genetic algorithm, Support Vector Machines