Wu Xuan, Song Yafei, Wang Yifei
School of Postgraduate School, Air Force Engineering University, Xi'an 710051, China.
School of Air and Missile Defense, Air Force Engineering University, Xi'an 710051, China.
Entropy (Basel). 2021 Aug 28;23(9):1119. doi: 10.3390/e23091119.
Much attention has been paid to construct an applicable knowledge measure or uncertainty measure for Atanassov's intuitionistic fuzzy set (AIFS). However, many of these measures were developed from intuitionistic fuzzy entropy, which cannot really reflect the knowledge amount associated with an AIFS well. Some knowledge measures were constructed based on the distinction between an AIFS and its complementary set, which may lead to information loss in decision making. In this paper, knowledge amount of an AIFS is quantified by calculating the distance from an AIFS to the AIFS with maximum uncertainty. Axiomatic properties for the definition of knowledge measure are extended to a more general level. Then the new knowledge measure is developed based on an intuitionistic fuzzy distance measure. The properties of the proposed distance-based knowledge measure are investigated based on mathematical analysis and numerical examples. The proposed knowledge measure is finally applied to solve the multi-attribute group decision-making (MAGDM) problem with intuitionistic fuzzy information. The new MAGDM method is used to evaluate the threat level of malicious code. Experimental results in malicious code threat evaluation demonstrate the effectiveness and validity of proposed method.
人们已经对构建适用于阿塔纳索夫直觉模糊集（AIFS）的知识度量或不确定性度量给予了很多关注。然而，这些度量中的许多都是从直觉模糊熵发展而来的，这并不能很好地真正反映与AIFS相关的知识量。一些知识度量是基于AIFS与其补集之间的差异构建的，这可能会导致决策中的信息丢失。在本文中，通过计算AIFS与具有最大不确定性的AIFS之间的距离来量化AIFS的知识量。知识度量定义的公理性质被扩展到更一般的层面。然后基于直觉模糊距离度量开发了新的知识度量。基于数学分析和数值示例研究了所提出的基于距离的知识度量的性质。所提出的知识度量最终被应用于解决具有直觉模糊信息的多属性群决策（MAGDM）问题。新的MAGDM方法用于评估恶意代码的威胁级别。恶意代码威胁评估的实验结果证明了所提方法的有效性和正确性。
