Minimum conflict consensus models for group decision-making based on social network analysis considering non-cooperative behaviors

Yuan, Yuxiang; Wang, Chaofa; Cheng, Dong; Zhang, Faming; Zhou, Zhili; Cheng, Faxin

doi:10.1016/j.inffus.2023.101855

Cited by 16 publications

(1 citation statement)

References 61 publications

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“…Compared to unweighted networks, weighted networks offer a richer expression in aspects like node centrality measurement and dynamic characteristics. For example, the multiscale self-similarity of weighted networks [34], the nonlinear relationship between degree and strength [35,36], among others. Moreover, network weights also have varying degrees of impact on dynamic processes such as disease transmission, percolation, and diffusion [37,38].…”

Section: Introductionmentioning

confidence: 99%

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

Yu,

Li,

et al. 2024

Meas. Sci. Technol.

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Feature selection is a crucial step in fault diagnosis. When rolling bearings are susceptible to compound faults, causal relationships are hidden within the signal features. Complex network analysis methods provide a tool for causal relationship modeling and feature importance assessment. Existing studies mainly focus on unweighted networks, overlooking the impact of the strength of causal relationships on feature selection. To address this issue, we propose a compound fault feature selection method based on the causal feature weighted network. First, we construct a weighted network using the incremental association Markov blanket discovery algorithm and Pearson correlation coefficient. Then, we quantify the importance of features by treating node strength as a centrality index and rank them to partition the feature subset. Finally, the optimal feature subset is obtained through a neural network with the accuracy of compound fault diagnosis as the threshold. Analysis of public datasets and comparative experiments demonstrate the advantages of our method. Compared to existing research, our method not only effectively reduces the number of optimal feature subsets to 11 but also improves the accuracy of compound fault diagnosis to 95.2%. Furthermore, we employ the SHapley Additive exPlanations to interpret the contribution of each feature in the optimal subset to the accuracy of compound fault diagnosis. This provides reference from both physical and network perspectives to feature selection and compound fault diagnosis in rolling bearings in practical working conditions.

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Section: Introductionmentioning

confidence: 99%

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

Yu,

Li,

et al. 2024

Meas. Sci. Technol.

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A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision

Chen,

Zhou,

Liu

2024

Cogn Comput

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A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network

Yang,

Zhang

2024

Complex Intell. Syst.

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A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.

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Minimum conflict consensus models for group decision-making based on social network analysis considering non-cooperative behaviors

Cited by 16 publications

References 61 publications

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision

A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network

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