The Unbalanced Linguistic Ordered Weighted Averaging operator

Isern, David; Marin, Lucas; Valls, Aïda; Moreno, Antonio

doi:10.1109/fuzzy.2010.5584199

Cited by 11 publications

(15 citation statements)

References 23 publications

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“…A lot of aggregatopn operators have been introduced in [28][29][30][31][32][33][34], Herrera, et al [11] put forword the arithmetic mean of linguistic 2-tuples to for unbalanced linguistic variables. Isern, et al [35] utilized the concepts of ordered weighted averaging operators to aggregate unbalanced linguistic variables. Han et al [36] gave an aggregation method for unbalanced linguistic information by untilizing the generalized Heronian mean operators.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Intuitionistic Unbalanced Linguistic Generalized Multiple Attribute Group Decision Making and Its Application to Green Products Selection

Han

Tao

Zhou

2018

Mathematical Problems in Engineering

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In many countries, green products play a critical role in energy recycling and environment protection. The selection of green products can be regarded as a multiple attribute decision making (MADM) problem. Due to the complexity and uncertainty of the problem, decision makers may give their personal preference values to different attributes of alternatives by intuitionistic unbalanced linguistic term sets. The main purpose of this paper is to put forward a new generalized multiple attribute group decision making (GMAGDM) approach based on the intuitionistic unbalanced linguistic dependent weighted generalized Heronian mean (IULDWGHM) operator and the intuitionistic unbalanced linguistic dependent weighted generalized geometric Heronian mean (IULDWGGHM) operator. The proposed method can not only relieve the influence of unfair assessments, but also consider the interaction effects of attributes. Furthermore, the appropriate parameter values and operators can be selected to meet the different risk preference of decision makers and actual requirements. Finally, a green products selection case is given to illustrate the effectiveness and universality of the developed approach.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Intuitionistic Unbalanced Linguistic Generalized Multiple Attribute Group Decision Making and Its Application to Green Products Selection

Han

Tao

Zhou

2018

Mathematical Problems in Engineering

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“…2 shows an example of these two kinds of scenario). The recommender platform that has been designed and implemented may use both kinds of terms during the aggregation process [16].…”

Section: Representation Of the User Profilementioning

confidence: 99%

“…, a n }, where a i 2 S. The system applies a linguistic aggregation operator to obtain an overall preference score for each of the alternatives. The aggregation operator that has been used is the Unbalanced Linguistic Ordered Weighted Averaging (ULOWA) operator, which is defined in [16] as follows:…”

Section: The Unsupervised Adaptation Algorithmmentioning

confidence: 99%

On-line dynamic adaptation of fuzzy preferences

Marin

Isern

Moreno

et al. 2013

Information Sciences

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“…It has been widely used in computational intelligence because of its ability to model linguistically expressed aggregation instructions. In [9,10,11], the authors put their works on the set of all alternatives and on the individual linguistic preference relations. Some choice processes are devoted to the model using consensus measures and LOWA operator.…”

Section: Related Researchmentioning

confidence: 99%

“…Secondly, we select a set of experts that are user and users' friends to evaluate the above methods by vague values. Thirdly, we solve those fuzzy relations and obtain the fuzzy collective solution by using group decision making in [9,10] for clustering and sorting the methods.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Decision Making-based Recommendation Channel System using the Social Network Database

Van¹,

Park²,

Jang³

et al. 2016

Journal of Digital Contents Society

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A user usually gets the same suggesting results as everyone else in most of the multimedia social services, nowadays. To address the challenging problem of personalization in the social network, we propose a method which exploits user's activities, user's moods, and user's friend relationships from the social network to build a decision-making system. Depending on a current state of the user's mood, this system infers the most appropriated video for the user. In the system, the user evaluates a set of the given recommendation methods which extract from the user's database social network and assigns a vague value to each method by a weight. Then, we find the fuzzy collection solution for the system and classify the set of methods into subsets, and order the subsets based on its local dominance to choose the best appropriate method. Finally, we conduct an experiment using the YouTube API with a lot of video types. The experiment result shows that the channel recommendation system appropriately affords the user's character, it is more satisfying than the current YouTube based on an evaluation of several users.

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The Unbalanced Linguistic Ordered Weighted Averaging operator

Cited by 11 publications

References 23 publications

Intuitionistic Unbalanced Linguistic Generalized Multiple Attribute Group Decision Making and Its Application to Green Products Selection

Intuitionistic Unbalanced Linguistic Generalized Multiple Attribute Group Decision Making and Its Application to Green Products Selection

On-line dynamic adaptation of fuzzy preferences

Fuzzy Decision Making-based Recommendation Channel System using the Social Network Database

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