一个基于XGBoost算法和受约束参数方法的多维决策框架
Xinxin Wang1, BingBing Zhang1, Zeshui Xu1
1Business School, Sichuan University, Chengdu, 610064, China.
Scientific reports
|February 5, 2025
概括
本研究引入了一种新的决策框架,使用嵌套的概率语言术语集和XGBoost算法来进行不确定的决策. 它有效地整合了定量数据和定性变量,以导出属性权重并创建用于实际应用的科学决策矩阵.
科学领域:
- 决策科学 决策科学 决策科学
- 数据分析数据分析数据分析.
- 机器学习 机器学习
背景情况:
- 由于数据的复杂性和不确定性,组织在决策方面面临挑战.
- 对可解释和数据驱动的分析方法的需求正在增加.
- 现有的方法可能无法充分处理定量数据和定性语言变量.
研究的目的:
- 提出一个多维的决策框架,整合定量数据和定性变量.
- 开发一种使用历史数据和机器学习计算属性权重的方法.
- 为在不确定的环境中合理决策提供科学决策矩阵.
主要方法:
- 利用嵌套的概率语言术语集用于决策建模.
- 应用了XGBoost算法来从历史数据中确定属性重要性和权重.
- 采用受约束的参数方法来确定语言变量的成员函数.
主要成果:
- 开发了一个灵活而稳定的决策框架.
- 通过整合数据和语言变量成功生成了科学决策矩阵.
- 通过银行信用评级案例研究证明了框架的有效性.
结论:
- 拟议的框架为捕获,整合和分析数据和语言变量提供了一个强大的方法.
- 它支持管理者和决策者确定属性权重和选择适当的决策方法.
- 该框架增强了不确定性和认知决策的理论理解和实际应用.
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