Research on Evaluation and Prediction for Enhancing the Innovation Capabilities of Manufacturing Enterprises
- DOI
- 10.2991/978-94-6463-752-6_28How to use a DOI?
- Keywords
- TOPSIS; MI; Bagging Regression; Machine Learning
- Abstract
To overcome the limitations of traditional evaluation methods, this study proposes a novel approach that combines TOPSIS analysis, mutual information (MI), and Bagging regression model to comprehensively assess the innovation capability of manufacturing enterprises. This innovative method not only provides quantitative indicators for enterprise innovation ability but also uncovers key factors influencing such ability through eigenvalue analysis. Moreover, it offers personalized innovation guidance based on evaluation results, aiming to help enterprises identify their unique innovation potential and develop feasible strategies. The findings from this research not only facilitate rational planning of the innovation path and enhancement of market competitiveness for enterprises but also contribute new ideas and methodologies to foster innovation in the manufacturing industry.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Zhuoxun Che AU - Yanxin Chen AU - Yanluo He AU - Yangna Yin AU - Qiuquan Guo PY - 2025 DA - 2025/06/07 TI - Research on Evaluation and Prediction for Enhancing the Innovation Capabilities of Manufacturing Enterprises BT - Proceedings of 2025 2nd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2025) PB - Atlantis Press SP - 268 EP - 275 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-752-6_28 DO - 10.2991/978-94-6463-752-6_28 ID - Che2025 ER -