Version 1
: Received: 10 October 2024 / Approved: 11 October 2024 / Online: 14 October 2024 (10:13:15 CEST)
How to cite:
Gong, C.; Lin, Y.; Cao, J.; Wang, J. Research on Enterprise Risk Decision Support System Optimization based on Ensemble Machine Learning. Preprints2024, 2024100948. https://doi.org/10.20944/preprints202410.0948.v1
Gong, C.; Lin, Y.; Cao, J.; Wang, J. Research on Enterprise Risk Decision Support System Optimization based on Ensemble Machine Learning. Preprints 2024, 2024100948. https://doi.org/10.20944/preprints202410.0948.v1
Gong, C.; Lin, Y.; Cao, J.; Wang, J. Research on Enterprise Risk Decision Support System Optimization based on Ensemble Machine Learning. Preprints2024, 2024100948. https://doi.org/10.20944/preprints202410.0948.v1
APA Style
Gong, C., Lin, Y., Cao, J., & Wang, J. (2024). Research on Enterprise Risk Decision Support System Optimization based on Ensemble Machine Learning. Preprints. https://doi.org/10.20944/preprints202410.0948.v1
Chicago/Turabian Style
Gong, C., Jinming Cao and Jun Wang. 2024 "Research on Enterprise Risk Decision Support System Optimization based on Ensemble Machine Learning" Preprints. https://doi.org/10.20944/preprints202410.0948.v1
Abstract
Since the advent of artificial intelligence, it has not only transformed the way we live, but is also accelerating the transformation of production methods. Machine learning is a pivotal technology that endows computers with intelligence. The primary driving force behind its advancement is the pursuit of rapid and precise knowledge acquisition. Nevertheless, the existing enterprise risk decision support system is inadequate in terms of both timeliness and effectiveness when confronted with the task of analysing vast quantities of data. Furthermore, it lacks the capacity to assimilate the expertise of managers and to facilitate interaction in an intuitive manner.By combining a variety of machine learning models, the ensemble learning method effectively leverages the advantages of different algorithms, thereby improving the accuracy and stability of risk prediction. Each model provides a unique perspective and decision-making boundaries when dealing with complex enterprise risk data, but a single model may not be adequate when faced with a particular data set or a particular type of risk. The ensemble method overcomes the problem of bias or overfitting that may be caused by a single model by combining the prediction results of multiple models, so as to obtain a more robust prediction effect. These methods optimize the final prediction results based on how well they perform during training by giving different weights to each model. This convergence strategy significantly improves the accuracy of enterprise risk assessment and helps to provide decision-makers with more reliable data support to make more informed risk management decisions in complex business environments.
Keywords
enterprise risk; decision support system; ensemble machine learning; strategies optimization
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
