Table of Content

Data-Driven Robust Group Decision-Making Optimization and Application

Submission Deadline: 14 March 2023 Submit to Special Issue

Guest Editors

Prof. Shaojian Qu, Nanjing University of Information Science and Technology, China
Prof. Zhichao Zheng, National University of Singapore, Singapore
Prof. Ying Ji, Shanghai University, China
Prof. Qingguo Bai, Qufu Normal University, China


At present, with the parallel of global integration and reverse integration, as well as the rapid development of Internet technology and communication technology, the environment of governments, enterprises and other organizations is becoming more and more complex. Some major decision-making problems urgently need to make group decision-making with the help of group wisdom. As the basic decision-making form of human social activities, group decision-making can take into account various interests and overcome the shortcomings of individual knowledge, information and ability. It has been widely used in many fields, such as emergency decision-making of major emergencies, major strategic decision-making of the government, logistics and supply chain management decision-making and so on.

However, with the rapid development and deep integration of information technology, a new chapter of digital life has been opened and people have been replaced into the era of big data. Because big data has the characteristics of large volume, diversity, dynamics and low value density, dynamic and socialized group decision-making in the big data environment brings new challenges to the decision-making field, which is worthy of further exploration. Through the data-driven method, the consistent or compromise scheme is more effective than the traditional decision-making method. Therefore, applying data-driven technology to carry out more research and innovation on group decision-making has extensive theoretical and practical significance.

The aim of this Special Issue is to solicit the latest research and review articles on group decision-making driven by data-driven. We hope to combine the two studies, including new theoretical methods based on existing theories. We welcome new ideas to explore the future development direction of intelligent group decision-making. Welcome to provide original contributions of novel theories, methods and applications to the problems of data-driven group decision-making research.

The main topics of this special issue include, but are not limited to, the following:

Application of robust optimization method in uncertain group decision-making

Application of data-driven method in group decision-making

Application of machine learning method in group decision-making

Clustering method of preference data in group decision-making

Multistage dynamic group decision-making method

Large group emergency decision-making based on decision maker behavior data mining

Data collection and extraction in online reviews

Data mining for feature learning, classification, regression and clustering 


Group decision-marking; Robust optimization; Data-driven; Large group emergency decision-making

Published Papers

  • Open Access


    Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

    Xuhui Zhu, Pingfan Xia, Qizhi He, Zhiwei Ni, Liping Ni
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 653-671, 2023, DOI:10.32604/cmes.2022.022985
    (This article belongs to this Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we introduce a combined diversity measure… More >

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