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Optimization of XGBoost Bankruptcy Prediction Based on Four-Vector Optimization Algorithm
- 1 Department of Economics, University of California, Berkeley, State of California, Berkeley, 94704, USA
* Author to whom correspondence should be addressed.
Abstract
In this paper, the XGBoost model is optimized based on the four-vector optimization algorithm to realize the effective prediction of enterprise bankruptcy. In the experiment, we adopted this advanced algorithm to greatly improve the predictive performance of the model, with the goal of providing a reliable bankruptcy risk assessment tool for enterprises and investors. By training the model, we observed a gradual decline in the fitness curve from 0.029 to 0.026. This trend indicates that with the increase of the number of training iterations, the performance of the model is constantly improving. The gradual slowing of fitness decline suggests that the model may have approached the optimal solution or become stable, which provides confidence for subsequent applications. In terms of specific prediction effect, the confusion matrix of the training set shows that the accuracy of the model is as high as 100%, while the accuracy of the test set is as high as 97.33%. Such high accuracy not only reflects the model's excellent ability on the seen data, but also shows that the model has good generalization ability and can maintain efficient performance on the unseen data. In addition, the performance indicator chart shows the trend of different performance indicators, including FM, J, Q, CA, DAUC, SE, SP. From the figure, we can see that the values of FM and J remain around 1.5, indicating that the model performs well in some ways. However, DAUC and SE showed less than ideal results, close to zero or negative, which means that on some performance indicators, the model still needs further optimization and improvement. To sum up, the research in this paper not only provides an effective tool for corporate bankruptcy prediction, but also provides data support for investors and managers in the decision-making process. By accurately predicting the risk of bankruptcy, relevant parties can take preventive measures to effectively reduce investment risks and improve decision-making efficiency. Therefore, improving the accuracy of corporate bankruptcy prediction can not only promote the sustainable development of enterprises, but also provide protection for the healthy operation of the economy in a larger scope.
Keywords
Four-vector optimization algorithm, XGBoost, Enterprise Bankruptcy prediction
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Cite this article
Jin,Y. (2024). Optimization of XGBoost Bankruptcy Prediction Based on Four-Vector Optimization Algorithm. Applied and Computational Engineering,120,42-49.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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