Browsing by Author "Kamis, Nor Hanimah"

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    A Framework of Directed Network Based Influence-Trust Fuzzy Group Decision Making
    (Springer Nature, 2023-08-17) Kamis, Nor Hanimah; Kilicman, Adem; Kadir, Norhidayah A.; Chiclana, Francisco
    Daily life requires individuals or groups of decision-makers to engage in critical decision-making processes. Fuzzy set theory has been integrated into group decision-making (GDM) to address the ambiguity and vagueness of expert preferences. Social Network Group Decision Making (SNGDM) is a newly emerging research area that focuses on the use of social networks to facilitate information exchange and communication among experts in GDM. Moreover, Social Influence Group Decision Making (SIGDM) has been initiated, which considers social influence as a factor that can impact experts’ preferences during interactions or discussions. Studies in this area have proposed innovative measurements of social influence, including the use of trust statements to explicitly influence experts’ opinions. In this study, a new trust index called TrustRank is proposed, which acts as an additional weightage of experts’ importance and is embedded in the influence network measure that represents the strength of the expert’s influence degree. These values are then utilized as the order-inducing variable in the IOWA-based fusion operator to obtain the collective preference and ranking of alternatives. The proposed framework, which is the directed network-based Influence-Trust Fuzzy GDM, is presented along with its implementation, results, and discussion to showcase its applicability.
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    Geo-uninorm Consistency Control Module for Preference Similarity Network Hierarchical Clustering Based Consensus Model
    (Elsevier, 2018-05-30) Kamis, Nor Hanimah; Chiclana, Francisco; Levesley, Jeremy
    "In order to avoid misleading decision solutions in group decision making (GDM) processes, in addition to consensus, which is ob- viously desirable to guarantee that the group of experts accept the final decision solution, consistency of information should also be sought after. For experts’ preferences represented by reciprocal fuzzy preference relations, consistency is linked to the transitivity property. In this study, we put forward a new consensus approach to solve GDM with reciprocal preference relations that imple- ments rationality criteria of consistency based on the transitivity property with the following twofold aim prior to finding the final decision solution: (A) to develop a consistency control module to provide personalized consistency feedback to inconsistent experts in the GDM problem to guarantee the consistency of preferences; and (B) to design a consistent preference network clustering based consensus measure based on an undirected weighted consistent preference similarity network structure with undirected complete links, which using the concept of structural equivalence will allow one to (i) cluster the experts; and (ii) measure their consensus status. Based on the uninorm characterization of consistency of reciprocal preferences relations and the geometric average, we propose the implementation of the geo-uninorm operator to derive a consistent based preference relation from a given reciprocal preference relation. This is subsequently used to measure the consistency level of a given preference relation as the cosine simi- larity between the respective relations’ essential vectors of preference intensity. The proposed geo-uninorm consistency measure will allow the building of a consistency control module based on a personalized feedback mechanism to be implemented when the consistency level is insufficient. This consistency control module has two advantages: (1) it guarantees consistency by advising inconsistent expert(s) to modify their preferences with minimum changes; and (2) it provides fair recommendations individually, depending on the experts’ personal level of inconsistency. Once consistency of preferences is guaranteed, a structural equivalence preference similarity network is constructed. For the purpose of representing structurally equivalent experts and measuring consen- sus within the group of experts, we develop an agglomerative hierarchical clustering based consensus algorithm, which can be used as a visualization tool in monitoring current state of experts’ group agreement and in controlling the decision making process. The proposed model is validated with a comparative analysis with an existing literature study, from which conclusions are drawn and explained"
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    Similarity-Trust Network For Clustering Based Consensus Group Decision Making Model
    (Wiley, 2021-08-16) Ahlim, Wan Syahimi Afiq Wan; Kamis, Nor Hanimah; Ahmad, Sharifah Aniza Sayed; Chiclana, Francisco
    Trust relation, as defined in Social Network Analysis(SNA), is one of the recent notions considered in de-cision making. This inspired our integration of trustrelation in constructing a similarity–trust network. Si-milarity of experts' preferences is analyzed inclusivelywith trust relation by defining a new combinationfunction of both attributes. The agglomerative hier-archical clustering approach is applied to group expertsinto subclusters based on the constructed similarity—trust degrees. The centrality concept from SNA is thenused to determine the expert's similarity–trust cen-trality (STC) index, which is the basis for the con-struction of a new aggregation operator, STC‐inducedordered weighted averaging operator, to fuse the in-dividual experts' preferences into a collective one, fromwhich the consensus solution is derived. An analysis ofresults with different levels of trust degree is carriedout. We show that this new idea is promising and re-levant to be used in solving certain consensus groupdecision‐making problems.
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    Social Influence in Fuzzy Group Decision Making with Applications
    (Springer, 2022-07-02) Abu Bakar Sedek, Amirah Nabilah Sedek; Kamis, Nor Hanimah; Kadir, Norhidayah A; Mohamad, Daud; Chiclana, Francisco
    Group decision making (GDM) is a process of evaluating multi-expert preferences towards criteria or alternatives in order to solve problems in daily life. The previous GDM models often neglected the discussion on experts’ relationships and interactions between them. The incorporation of Social Network Analysis (SNA) in decision making context provides measurement of experts’ relations and interactions in the group. The experts normally have different background, status, position, level of knowledge, expertise, experience etc., in which the group decision might be changed or influenced. The influence notion in Social Influence Network (SIN) plays an important role in defining the influence element in decision making perspective. This new area of study is known as Social Influence Group Decision Making (SIGDM). This paper proposed the framework of the similarity-influence network based fuzzy group decision making model. The influence measure is utilized to identify the most influential expert in the network. For the purpose of aggregating all individual expert preferences into a collective one, the influence-based aggregation operator is used. This paper discusses on the possible applications and future works related to the SIGDM.