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

<p>This study suggests a PSO-SVM financial crisis early warning model aid organizations in developing an efficient financial crisis early warning model for early financial crisis avoidance. For feature set selection, a wrapper method is utilized, in which the classifier’s learning algorithm and feature set selection are combined. The SVM is utilized as the classifier, and its feature set and kernel function parameters (<italic>C</italic> and <italic>δ</italic><sup>2</sup>) are employed as the placements of the particles in the PSO, with the SVM’s classification results being used as the adaption values. By achieving the near-optimal feature subset and kernel function parameters, the results are close to the model’s ideal predictions. The PSO technique is used to concurrently optimize the feature set and the kernel function parameters to remove unnecessary or redundant features. According to the findings of the model comparison, the SVM’s accuracy is 80.31%, which is less than the accuracy of the PSO-SVM model, which is 94.55%. The accuracy and recall of the PSO-SVM training set are 71.39% and 81.87%, respectively, while the accuracy and recall of the test set are both 70.53% and 72.29%, respectively. In comparison, the SVM model’s test set accuracy rate is just 34.57%, while its training set accuracy rate is only 44.01%. This suggests that the PSO-SVM model put forward in this research has seen significant improvements in both the accuracy of indicator selection and indicator interpretability.</p>
</abstract>

This study suggests a PSO-SVM financial crisis early warning model aid organizations in developing an efficient financial crisis early warning model for early financial crisis avoidance. For feature set selection, a wrapper method is utilized, in which the classifier’s learning algorithm and feature set selection are combined. The SVM is utilized as the classifier, and its feature set and kernel function parameters (C and δ2) are employed as the placements of the particles in the PSO, with the SVM’s classification results being used as the adaption values. By achieving the near-optimal feature subset and kernel function parameters, the results are close to the model’s ideal predictions. The PSO technique is used to concurrently optimize the feature set and the kernel function parameters to remove unnecessary or redundant features. According to the findings of the model comparison, the SVM’s accuracy is 80.31%, which is less than the accuracy of the PSO-SVM model, which is 94.55%. The accuracy and recall of the PSO-SVM training set are 71.39% and 81.87%, respectively, while the accuracy and recall of the test set are both 70.53% and 72.29%, respectively. In comparison, the SVM model’s test set accuracy rate is just 34.57%, while its training set accuracy rate is only 44.01%. This suggests that the PSO-SVM model put forward in this research has seen significant improvements in both the accuracy of indicator selection and indicator interpretability.

Research on the construction of enterprise financial management information based on feature set

Applied Mathematics and Nonlinear Sciences

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

This study suggests a PSO-SVM financial crisis early warning model aid organizations in developing an efficient financial crisis early warning model for early...

Research on the construction of enterprise financial management information based on feature set

Publié en ligne: 04 oct. 2023

Pages: -

Reçu: 03 nov. 2022

Accepté: 14 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00484

Mots clés
Corporate financial management, PSO, SVM, Feature set, Kernel function

© 2023 Yingying Jin, published by Sciendo

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

Research on the construction of enterprise financial management information based on feature set

Publié en ligne: 04 oct. 2023

Pages: -

Reçu: 03 nov. 2022

Accepté: 14 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00484

Mots clésCorporate financial management, PSO, SVM, Feature set, Kernel function

© 2023 Yingying Jin, published by Sciendo

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

Mots clés
Corporate financial management, PSO, SVM, Feature set, Kernel function