Measuring agreement on linguistic expressions in medical treatment scenarios

Navarroa, J.; Wagnerab, C.; Aickelin, Uwe; Greend, L.; Ashfordd, R.

doi:10.1109/ssci.2016.7849895

Cited by 7 publications

(14 citation statements)

References 17 publications

(34 reference statements)

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“…As each α-cut of a normal and convex fuzzy set is a closed interval [22], we aim to extend the proposed similarity measure for comparing fuzzy sets. …”

Section: Discussionmentioning

confidence: 99%

Novel similarity measure for interval-valued data based on overlapping ratio

Kabir

Wagner

Havens

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-In computing the similarity of intervals, current similarity measures such as the commonly used Jaccard and Dice measures are at times not sensitive to changes in the width of intervals, producing equal similarities for substantially different pairs of intervals. To address this, we propose a new similarity measure that uses a bi-directional approach to determine interval similarity. For each direction, the overlapping ratio of the given interval in a pair with the other interval is used as a measure of uni-directional similarity. We show that the proposed measure satisfies all common properties of a similarity measure, while also being invariant in respect to multiplication of the interval endpoints and exhibiting linear growth in respect to linearly increasing overlap. Further, we compare the behavior of the proposed measure with the highly popular Jaccard and Dice similarity measures, highlighting that the proposed approach is more sensitive to changes in interval widths. Finally, we show that the proposed similarity is bounded by the Jaccard and the Dice similarity, thus providing a reliable alternative.

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“…As each α-cut of a normal and convex fuzzy set is a closed interval [22], we aim to extend the proposed similarity measure for comparing fuzzy sets. …”

Section: Discussionmentioning

confidence: 99%

Novel similarity measure for interval-valued data based on overlapping ratio

Kabir

Wagner

Havens

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…The agreement ratio, Alg (2), proposed by Navarro et al [12] is a measure within the range of 0 and 1 of the agreement amongst the data sources (e.g. experts) that have provided their opinion as intervals.…”

Section: Agreement Ratiomentioning

confidence: 99%

Similarity measure for aggregated fuzzy numbers from interval-valued data

Gunn

Khorshidi

Aickelin

2020

Soft Computing Letters

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This paper presents a method to compute the degree of similarity between two aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA). The similarity measure proposed within this study contains several features and attributes, of which are novel to aggregated fuzzy numbers. The attributes completely redefined or modified within this study include area, perimeter, centroids, quartiles and the agreement ratio. The recommended weighting for each feature has been learned using Principal Component Analysis (PCA). Furthermore, an illustrative example is provided to detail the application and potential future use of the similarity measure.

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“…These weights help the decision-making process becomes more robust and less sensitive to dispersed opinions of the decision-makers. In addition, finding the level of agreement of decision-makers has always been important in decision-making processes [19]. We use a bi-level optimisation model to find the weights of the criteria as Eq.…”

Section: Stage 2: Decision Matrix Construction 1) Construct the Decismentioning

confidence: 99%

“…, for all (18) 3) Determine the ranking score Once the distances from ideal solutions are calculated for all alternatives, a ranking score ( % ) can be formulated based on how much an alternative is close to the positive ideal solution and distant from the negative ideal solution as Eq. (19).…”

Section: ) Define Ideal Solutionsmentioning

confidence: 99%

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Multicriteria group decision-making under uncertainty using interval data and cloud models

Khorshidi

Aickelin

2020

Journal of the Operational Research Society

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In this study, we propose a multicriteria group decision-making (MCGDM) algorithm under uncertainty where data is collected as intervals. The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input. The intervals give flexibility to experts in assessing alternatives against criteria and provide an opportunity to gain maximum information. We also propose a novel method to aggregate experts' judgements using cloud models. We introduce an experimental approach to check the validity of the aggregation method. After that, we use the aggregation method for an MCGDM problem. Here, we find the optimal weights for each criterion by proposing a bi-level optimisation model. Then, we extend the technique for order of preference by similarity to ideal solution (TOPSIS) for data based on cloud models to prioritise alternatives. As a result, the algorithm can gain information from decisionmakers with different levels of uncertainty and examine alternatives with no more information from decisionmakers. The proposed MCGDM algorithm is implemented on a case study of a cybersecurity problem to illustrate its feasibility and effectiveness. The results verify the robustness and validity of the proposed MCGDM using sensitivity analysis and comparison with other existing algorithms.

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Measuring agreement on linguistic expressions in medical treatment scenarios

Cited by 7 publications

References 17 publications

Novel similarity measure for interval-valued data based on overlapping ratio

Novel similarity measure for interval-valued data based on overlapping ratio

Similarity measure for aggregated fuzzy numbers from interval-valued data

Multicriteria group decision-making under uncertainty using interval data and cloud models

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