On Generalized Extended Bonferroni Means for Decision Making
On Generalized Extended Bonferroni Means for Decision Making
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DOI：
10.1109/tfuzz.2016.2540066
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发表时间：
2016-12
影响因子：
11.9
通讯作者：
Zhen-Song Chen;K. Chin;Yan-Lai Li;Yi Yang
中科院分区：
文献类型：
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作者：
Zhen-Song Chen;K. Chin;Yan-Lai Li;Yi Yang
最近提出的扩展Bonferroni均值(EBM)不同于经典的Bonferroni均值，它的目的是捕捉属性之间的异质相互关系，而不是假设属性之间的齐次关系。在这项研究中，我们通过定义一个复合聚集函数，对EBM进行了推广，以明确而深刻地理解其聚集机制。我们采用了基于残差最小绝对偏差的广义EBM(GEBM)加权向量的优化选择方法。我们还研究了GEBM的几个理想性质。我们在这项研究中特别感兴趣的是调查GEBM模拟强制性要求的能力。最后，分析了GEBM中合取词替换的影响，说明了合取词的变化是如何影响GEBM的全局AND和ORITY的。同时，对EBM的聚集机制进行了详细的描述，并为应用提供了相当直观的解释。
The extended Bonferroni mean (EBM) recently proposed differs from the classical Bonferroni mean, as it aims to capture the heterogeneous interrelationship among the attributes instead of presupposing a homogeneous relation among them. In this study, we generalize the EBM to explicitly and profoundly understand its aggregation mechanism by defining a composite aggregation function. We adopt the approach of optimizing the choice of weighting vectors for the generalized EBM (GEBM) with respect to the least absolute deviation of residuals. We also investigate several desirable properties of the GEBM. Our special interest in this study is to investigate the ability of the GEBM to model mandatory requirements. Finally, the influence of replacing the conjunctive of the GEBM is analyzed to show how the change of the conjunctive affects the global andness and orness of the GEBM. Meanwhile, the aggregation mechanism of the EBM is specified and provided with quite intuitive interpretations for application.