Large-Scale Group Decision-Making Method Using Hesitant Fuzzy Rule-Based Network for Asset Allocation
Abdul Malek Yaakob,
Shahira Shafie,
Alexander Gegov,
Siti Fatimah Abdul Rahman,
Ku Muhammad Naim Ku Khalif
Affiliations
Abdul Malek Yaakob
Department of Mathematics & Statistics, School of Quantitative Sciences, Universiti Utara Malaysia (UUM), Sintok 06010, Malaysia
Shahira Shafie
Department of Mathematics & Statistics, School of Quantitative Sciences, Universiti Utara Malaysia (UUM), Sintok 06010, Malaysia
Alexander Gegov
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
Siti Fatimah Abdul Rahman
School of Mathematical Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA (UiTM) Perlis Branch, Arau Campus, Arau 02600, Malaysia
Ku Muhammad Naim Ku Khalif
Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan 26300, Malaysia
Large-scale group decision-making (LSGDM) has become common in the new era of technology development involving a large number of experts. Recently, in the use of social network analysis (SNA), the community detection method has been highlighted by researchers as a useful method in handling the complexity of LSGDM. However, it is still challenging to deal with the reliability and hesitancy of information as well as the interpretability of the method. For this reason, we introduce a new approach of a Z-hesitant fuzzy network with the community detection method being put into practice for stock selection. The proposed approach was subsequently compared to an established approach in order to evaluate its applicability and efficacy.
