Three-way decision based on third-generation prospect theory with Z-numbers

Wang, Tianxing; Li, Huaxiong; Zhou, Xianzhong; Liu, Dun; Huang, Bing

doi:10.1016/j.ins.2021.04.001

Cited by 53 publications

(11 citation statements)

References 48 publications

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“…In the research regarding decision-making methods, some scholars have introduced fuzzy number operators to decision-making activities, which can provide methodical support for the operation of decision-making information [25][26][27][28][29]. Moreover, some decision methods, such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [30], VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method [31], Elimination and Choice Expressing Reality (ELECTRE) method [32,33], and the prospect theory [34], are widely used in various types of decision problems. Some scholars have used social network analysis to rank schemes based on the relationship and trust between decision-makers [35,36].…”

Section: Introductionmentioning

confidence: 99%

A Public-Participation-Based Mixed Multiattribute Decision-Making Approach for Major Public Affairs

Cai

Luo

Zhu

et al. 2021

Mathematical Problems in Engineering

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The decision-making activities of major public affairs are closely related to the public, so the decision results of such affairs must be supported by the public. The public must participate in decision-making activities to ensure their effectiveness, which further increases the complexity. In addition, attribute information and public opinion usually present different forms of expression in this type of problem, making decision-making more difficult. Therefore, a suitable decision approach must be chosen to deal with this type of decision problem. This paper addresses the decision-making characteristics of major public affairs and proposes a public-participation-based decision-making approach for mixed multiattribute decision-making problems in major public affairs. The proposed approach can work with entirely unknown attribute weights and decision-making values represented in multiple formats. First, the statistical distribution of public opinions is determined based on the expectation of the attributes, resulting in decision-making reference points for various attributes. The different forms of attributes and reference points are unified. Then, the values of the attributes and reference points are standardized. Afterward, the attribute prospect value for each alternative is calculated using the attribute value and corresponding reference points. The attribute weight intervals are determined based on the importance information of the attributes provided by the public. An optimization model is established to determine the attribute weights to maximize the alternative attribute deviation. Next, the comprehensive prospect value of each alternative is obtained to determine the ranking of the alternatives. Finally, a case analysis is conducted with a method comparison and sensitivity analysis, and the feasibility and effectiveness of the proposed approach are verified. In the proposed method, the reference points for each attribute are set according to the distribution characteristics and ambiguity of public expectations, guaranteeing that public expectations can be effectively reflected in the attribute reference points. In the process of attribute weighting, based on the information for the attribute importance given by the public, the range of attribute weights is determined. Then, we obtain the exact value of the attribute weights using an optimization model to maximize the alternative attribute deviation. The final result of the attribute weights ensures the full expression of public opinion and can improve the differentiation of decision results, which is convenient for ranking alternatives. During evaluation of the alternatives based on prospect theory, the expression forms of attributes and reference points are unified. Subsequently, the values of them are normalized, which satisfy the decision-making requirement of major public affairs.

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Section: Introductionmentioning

confidence: 99%

A Public-Participation-Based Mixed Multiattribute Decision-Making Approach for Major Public Affairs

Cai

Luo

Zhu

et al. 2021

Mathematical Problems in Engineering

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“…Suppose

A = \{ {\langle {x,{\mu _A}( x ),{\nu _A}(x)} \rangle | {x \in [ {0,1} ]} } \}

is an IVIFN membership function, and

B = \{ {\langle {y,{\mu _B}( y )} \rangle | {y \in [ {0,1} ]} } \}

is a triangular fuzzy membership function. Motivated by the references, 53,54 a Z‐number can be converted into a Z‐IVIFN value that follows the form of the conventional IVIFN by the following calculations: Convert the judgmental reliability

B

of the component

A

into a crisp number by

\begin{equation}\alpha = \frac{{\int{{y{\mu _B}\left( y \right)dy}}}}{{\int{{{\mu _B}\left( y \right)dy}}}},\end{equation}

where ∫ denotes an algebraic integration. Add the weight value of judgmental reliability

B

to the fuzzy restriction

A

, then the weighted Z‐number can be presented as follows:

{\tilde{Z}}^{α} badbreak= ({}, (〈〉, x, μ_{A^{α}} ((), x)) (|, μ_{A^{α}} ((), x) goodbreak= α μ_{A} ((), x),))

…”

Section: Preliminariesmentioning

confidence: 99%

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Xiao

Huang

Zhang

2022

Quality & Reliability Eng

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As a systematic risk assessment instrument, failure mode, and effects analysis (FMEA) has been extensively applied for enhancing systems’ quality. However, the classical FMEA technique has limitations in many application scenarios. Although fuzzy logic‐based risk assessment techniques are introduced to enhance conventional FMEA, they still have limitations, for example, needing prior presumptions, neglecting decision‐makers’ bounded rationality, and lacking the capability to reflect the judgmental reliability and randomness of experts’ evaluations simultaneously. To cover these limitations, an extended FMEA method is presented in this paper by using Z‐fuzzy clouds and generalized TODIM (An acronym in Portuguese of interactive and multiple attribute decision‐making). First, a novel concept of Z‐fuzzy clouds is proposed to handle various factors of uncertainties by combining the advantages of the Z‐number theory, the interval‐valued intuitionistic fuzzy sets theory, and the cloud model theory, and the basic operations and Minkowski‐type distance measure of Z‐fuzzy clouds are also defined and discussed. Furthermore, more risk factors are added into the conventional three risk factors and to further build a multiple dimension risk factor system for better evaluating failures. Additionally, the experts’ bounded rationality and the risk factors’ interrelationships are considered by two developed weighting models. Finally, a real study case, sensitivity analysis, and several comparisons are performed to validate the effectiveness of the proposed model. Results demonstrate that the proposed model is more rational compared with the previous several methods.

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“…To solve the problems existing in the application of traditional clustering algorithms in the intrusion detection systems, many scholars have improved the two-way clustering algorithm by introducing the three-way decision idea into the clustering algorithm and then proposed the three-way clustering method. The core idea is to extend the decision items into positive domain decision, negative domain decision, and boundary domain decision [16,17]. If you have a full grasp of and a comprehensive understanding of things, you can directly make a judgment of acceptance or rejection; otherwise, further investigation is manifested as a delay in decision making [18,19].…”

Section: The Improved K-means Clustering Algorithm Bymentioning

confidence: 99%

An Improved K‐Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

Xie

Dong

Wang

2021

Wireless Communications and Mobile Computing

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The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

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Three-way decision based on third-generation prospect theory with Z-numbers

Cited by 53 publications

References 48 publications

A Public-Participation-Based Mixed Multiattribute Decision-Making Approach for Major Public Affairs

A Public-Participation-Based Mixed Multiattribute Decision-Making Approach for Major Public Affairs

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

An Improved K‐Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

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