Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization

Lu, Yanling; Xu, Yang; Herrera‐Viedma, Enrique; Han, Yefan

doi:10.1016/j.ins.2020.08.022

Cited by 187 publications

(31 citation statements)

References 49 publications

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“…Based on existing research on HUI mining, several variant algorithms have been proposed, e.g., high average-utility mining [26][27][28], Top-K high utility mining [29,30], HUI mining from data stream [31,32], high-utility association rules [33], multi-core or parallel mining [34,35], and HUIM over multiple data sources [36]. Most of these studies mainly apply methods of one phase or two phase.…”

Section: Variant Algorithmsmentioning

confidence: 99%

HUIL-TN & HUI-TN: Mining high utility itemsets based on pattern-growth

Wang

2021

PLoS ONE

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In recent years, high utility itemsets (HUIs) mining has been an active research topic in data mining. In this study, we propose two efficient pattern-growth based HUI mining algorithms, called High Utility Itemset based on Length and Tail-Node tree (HUIL-TN) and High Utility Itemset based on Tail-Node tree (HUI-TN). These two algorithms avoid the time-consuming candidate generation stage and the need of scanning the original dataset multiple times for exact utility values. A novel tree structure, named tail-node tree (TN-tree) is proposed as a key element of our algorithms to maintain complete utililty-information of existing itemsets of a dataset. The performance of HUIL-TN and HUI-TN was evaluated against state-of-the-art reference methods on various datasets. Experimental results showed that our algorithms exceed or close to the best performance on all datasets in terms of running time, while other algorithms can only excel in certain types of dataset. Scalability tests were also performed and our algorithms obtained the flattest curves among all competitors.

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Section: Variant Algorithmsmentioning

confidence: 99%

HUIL-TN & HUI-TN: Mining high utility itemsets based on pattern-growth

Wang

2021

PLoS ONE

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“…With the development of the natural and economic environment, MAGDM problems have evolved from individual decision-making and small-scale group decision-making (GDM) to complex large-scale GDM (LSGDM) (Zhang et al 2014 ; Jin et al 2020 ; Xu et al 2019a , b ). Generally, LSGDM problems have the following characteristics (Xu et al 2015 ; Lu et al 2021 ; Guo et al 2020 ; Quesada et al 2015 ): Decision makers (DMs) can participate in decision-making at different times and places by using network tools; Generally, more than 20 DMs are used to handle LSGDM problems (Chen and Liu 2006 ), including people in both the same organization and different organizations, and, the knowledge, skills, experience and personality characteristics of each DM often vary. Besides, DMs both cooperate and compete; The relationship between attributes is not only interrelated but also contradictory; DMs’ assessments are uncertain and diverse, and consequently often quite conflicting.…”

Section: Introductionmentioning

confidence: 99%

Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory

et al. 2021

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“…For a recent paper that revisits fuzzy and linguistic decision-making, the reader is referred to [5]. Some other modern challenges faced by researchers in GDM are: (i) large-scale problems (LSGDM) involving more than twenty decision-makers (e.g., [6,7]); and (ii) manipulation and group dictatorship (e.g., [8]). These challenges have gained increasing importance as a consequence of the modern digital economy and the relevance of social networks.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

et al. 2021

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This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

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Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization

Cited by 187 publications

References 49 publications

HUIL-TN & HUI-TN: Mining high utility itemsets based on pattern-growth

HUIL-TN & HUI-TN: Mining high utility itemsets based on pattern-growth

Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory

A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

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