| 引用本文: | 宋 颖1,王 健1,吴 涛1,2.一种大规模群决策中评价信息特征提取方法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2023，40（5）：96-103 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 针对大规模群决策问题(Large-scale Group Decision Problem,LGDP),在多粒度不平衡犹豫语言模糊环境下,
提出了一种决策者评价信息特征提取的方法,同时考虑到决策者们聚类后形成的不同集群间的权重会受其内决策
者差异的影响,定义了离散系数,用于修正集群间的权重;首先,对决策者提供的多粒度语言进行一致化,并得到具
有概率信息的决策矩阵;其次,在计算机视觉分析中,任意图像都是由 RGB 三基色构成,且图像相比于数据更易进
行特征提取,故通过计算决策矩阵中的所有概率数据对应的 RGB 值得到对应的彩色图像,运用特征提取算法提取
决策矩阵中评价信息的特征,避免了现有决策方法难以快速有效提取决策矩阵中关键特征的缺点,且在处理大规
模决策问题时更高效和简洁;之后进一步对决策者进行聚类得到不同的集群,以新定义的离散系数来得到修正后
的集群间权重,然后通过计算净流大小来对方案排序得到最终决策结果;最后,以铁路线路方案的选择为例,说明
了方法的有效性和可行性。 |
| 关键词: 决策 多粒度 不平衡犹豫语言 特征提取 |
| DOI： |
| 分类号: |
| 基金项目: |
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| A Method for Extracting Evaluation Information Features in Large-scale Group Decision Making |
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SONG Ying1, WANG Jian1, WU Tao1 2
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1. School of Mathematical Science Anhui University Hefei 230601 China
2. Key Laboratory of Intelligence Computing and Signal Processing of Ministry of Education Anhui University Hefei
230601 China
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| Abstract: |
| Aiming at the large-scale group decision problem LGDP a feature extraction method for decision makers??
evaluation information was proposed under the fuzzy environment of multi-granularity unbalanced hesitant language.
Considering that the weights of different clusters formed by decision makers after clustering are affected by the differences
of their decision makers a discrete coefficient is defined to correct the weights between clusters. Firstly the multigranularity language provided by decision makers was unified and the decision matrix with probability information was
obtained. Secondly in computer vision analysis any image is composed of RGB three primary colors and the image is
easier to extract feature than the data so the corresponding color image was obtained by calculating the RGB
corresponding to all probability data in the decision matrix. The feature extraction algorithm was used to extract the characteristics of the evaluation information in the decision matrix which avoided the shortcomings of the existing decision
methods that are difficult to extract the key features in the decision matrix quickly and effectively and were more efficient
and concise in dealing with large-scale decision problems. After that the decision makers were further clustered to obtain
different clusters and the revised weight between clusters was obtained by using the newly defined discrete coefficient.
Then the final decision result was obtained by calculating the net flow size to sort the schemes. Finally the effectiveness
and feasibility of the method were illustrated by an example of railway line scheme selection. |
| Key words: decision making multi-granularity unbalanced hesitant language feature extraction |
