最大透-最小残余模型:对综合评估和多种属性决策的最佳解决方案
Qi-Yi Tang1, Yu-Xuan Lin2,3
1Institute of Insect Sciences, Zhejiang University, Hangzhou 310028, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
本研究引入了一种新的最大透-最小残余 (MEMR) 模型,用于创建复合指标. 在综合性评估中,MEMR提供了一种可靠和可解释的方法来分配因素权重.
科学领域:
- 多学科 多学科
- 数据科学数据科学数据科学
- 应用数学 应用数学 应用数学
背景情况:
- 综合指标对于整体评估至关重要,但在权重分配方面面临挑战.
- 现有的权衡方法可以产生微不足道,反直觉或计算不可行的结果.
研究的目的:
- 为生成复合指标权重提出一种新的最大透-最小残余 (MEMR) 模型.
- 为了解决当前权重方法的局限性.
主要方法:
- 开发了一个基于最大-最小余量 (MEMR) 原则的新模型.
- 直接估计了因子权重和复合指标之间的关系.
- 通过案例研究,将MEMR与现有的权重方法进行比较.
主要成果:
- MEMR模型有效地提取了共同特征,同时保持了因素多样性.
- 与其他方法相比,MEMR显示出更强大,更一致,更易于解释的结果.
- 该模型适用于使用定量因素进行全面评估.
结论:
- MEMR模型为复合指标重量生成提供了一种优越的方法.
- 该方法是多功能和适用于各种领域.
- 相关的优化技术和统计测试在DPS软件中可用.
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