Confidence levels<i>q</i>‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Joshi, Bhagawati Prasad; Gegov, Alexander

doi:10.1002/int.22203

Cited by 63 publications

(34 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a stronger tool than IFS in dealing with probability information and uncertainty information, it could process that the decision information with the sum of n (n ≥ 1) power of positive MBSD and negative MBSD is greater than 1 but the sum of q (q > n) power of them is bound to 1. q-ROFS has attracted a lot of attentions, and many authors have successively carried out researches from the following aspects, including basic properties, 42,43 basic operational rules, 44,45 and information aggregation operators. [46][47][48] Whereas, the internal interactivities between MBSDs of decision-making units as well as the interrelationship between attributes are not involved in the above researches. He et al 49 proposed the interaction operators between MBSDs of IFS earlier.…”

Section: Fusion For Multiple Sentiment Orientations Of Online Reviewsmentioning

confidence: 99%

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Yang

Ouyang

et al. 2020

Int J Intell Syst

View full text Add to dashboard Cite

In the process of online shopping, consumers usually compare the review information of the same product in different e‐commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep‐learning–based opinion mining and q‐rung orthopair fuzzy interaction weighted Heronian mean (q‐ROFIWHM) operators. First, in the proposed method, the deep‐learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute‐opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross‐decision matrices, which are represented by the q‐rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q‐ROFIWHM operators are proposed to aggregate this cross‐decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.

show abstract

Section: Fusion For Multiple Sentiment Orientations Of Online Reviewsmentioning

confidence: 99%

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Yang

Ouyang

et al. 2020

Int J Intell Syst

View full text Add to dashboard Cite

show abstract

“…The decision makers conversance (called confidence level) is very important perspective in solving

sans-serifMAGDM

problems. In

sans-serifqROFSs

, the confidence level perspective was used by Joshi and Gegove 54 …”

Section: Preliminariesmentioning

confidence: 99%

“…">Normalize the

sans-serifqROF

decision matrix

A^{ℓ}

for each expert according to the benefit and cost criteria as follows:

a_{ij}^{ℓ} = ({, \begin{array}{cl} (ξ_{i j}, ν_{i j}), & for benefit criteria, \\ (ν_{i j}, ξ_{i j}), & for cost criteria . \end{array})

Since the knowledge measure

I_{t}^{q}

is symmetric, therefore, the value of confidence level for each

sans-serifqROFV

remains unchanged. For each normalized

sans-serifqROF

decision matrix

A^{ℓ}

, we apply

sans-serifqROF

weighted average operator under confidence level

(qWAC)

proposed by Joshi and Gegov 54 to aggregate the information from each

sans-serifqROF

decision matrix

A^{ℓ}

as follows:

\begin{matrix} left0.33em \\ z_{i}^{ℓ} & = & {qWAC}_{ϖ} (a_{i 1}^{ℓ}, a_{i 2}^{ℓ}, …, a_{in}^{ℓ} true)) \\ = & ϖ_{1} a_{i 1}^{ℓ} \oplus ϖ_{2} a_{i 2}^{ℓ} \oplus ⋯ \oplus ϖ_{n} a_{in}^{ℓ} \\ = & <{(][, 1 - \prod_{j = 1}^{n} {(1 - {(ξ_{ij}^{ℓ}))}^{q} true))}^{λ_{ij}^{ℓ} ϖ_{j}})}^{1 ∕ q}, <...>> \end{matrix}

…”

Section: Applications Of Knowledge Measure In Magdm Under Confidence mentioning

confidence: 99%

See 1 more Smart Citation

Knowledge measure for the q‐rung orthopair fuzzy sets

2020

View full text Add to dashboard Cite

“…A q-ROFS is characterized by the degrees of membership and nonmembership, whose sum of the qth power is not exceeding one. 14 When the rung q increases, more orthopairs can meet the restricted condition and then a wider scope of fuzzy information can be expressed. 15,16 The q-ROFSs offer more flexibility and freedom for decision makers to express their uncertain information by adjusting the value of the parameter q dynamically.…”

Section: Introductionmentioning

confidence: 99%

Undergraduate teaching audit and evaluation using an extended MABAC method under q ‐rung orthopair fuzzy environment

Gong

Luo

et al. 2020

Int J Intell Syst

View full text Add to dashboard Cite

Undergraduate teaching audit and evaluation (UTAE) is a new type of evaluation pattern, which is extremely important for a university to improve its quality assurance system and enhance teaching quality. Selecting an optimal university for benchmarking through UTAE to promote the quality of teaching can be regarded as a complex multicriteria decision making (MCDM) problem. Furthermore, in the process of UTAE, experts' evaluations over the teaching quality of universities are often imprecise and fuzzy due to the subjective nature of human thinking. In this paper, we propose a new UTAE approach based on q-rung orthopair fuzzy sets and the multiattribute border approximation area comparison (MABAC) method for evaluating and selecting the best university for benchmarking. The introduced method deals with the linguistic assessments given by experts by using q-ROFSs, assigns the weights of audit elements based on the indifference threshold-based attribute ratio analysis method, and acquires the ranking of universities with an extended MABAC method. The feasibility and effectiveness of the proposed q-rung orthopair fuzzy MABAC method is demonstrated through a realistic UTAE example. Results show that the UTAE method being proposed is valid and practical for UTAE.

show abstract

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Cited by 63 publications

References 47 publications

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Knowledge measure for the q‐rung orthopair fuzzy sets

Undergraduate teaching audit and evaluation using an extended MABAC method under q ‐rung orthopair fuzzy environment

Contact Info

Product

Resources

About