Research on Multi-dimensional Model Adaptation and Optimization Evaluation of Military Material Suppliers
- DOI
- 10.2991/978-94-6463-996-4_14How to use a DOI?
- Keywords
- Military Material Supplier; Multi-dimensional Evaluation; Machine Learning; Feature Extraction; Model Adaptation
- Abstract
In view of the problems of dimension separation and insufficient dynamics in supplier evaluation in military material procurement, this paper proposes an intelligent evaluation framework that integrates multi-dimensional model adaptation and optimization. Build a multi-dimensional tag system by integrating relevant data, preprocessing and coding standardization, multi-modal feature extraction technology is used to process the data, extract key features, integrate multiple regression algorithms and semi-supervised models, select the optimal model in each dimension according to “Dimension → Model → Indicator”, and visually display the supplier’s corporate portrait through a radar chart. The results show that the accuracy of predicting high-risk suppliers in each dimension of this method is significantly improved, and it can provide scientific support for military material procurement decisions.
- Copyright
- © 2026 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 - Bo Li AU - Lipeng Cao AU - Haifeng Ling AU - Kun Yang AU - Detian Chu PY - 2026 DA - 2026/02/15 TI - Research on Multi-dimensional Model Adaptation and Optimization Evaluation of Military Material Suppliers BT - Proceedings of the 2025 7th Management Science Informatization and Economic Innovation Development Conference (MSIEID 2025) PB - Atlantis Press SP - 160 EP - 173 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-996-4_14 DO - 10.2991/978-94-6463-996-4_14 ID - Li2026 ER -