Executive summaries of uncertain values close to the gain/loss threshold – linguistic modelling perspective

Stoklasa, Jan; Talášek, Tomáš; Stoklasová, Jana

doi:10.1016/j.eswa.2019.113108

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…Linguistic approximation is a process of assigning linguistic expressions to various mathematical objects that are obtained as outputs of fuzzy models [42]. In the literature, we can find various approaches to linguistic approximation, like the "best-fit" methods, linguistic hedges and connectives, etc.…”

Section: Linguistic Approximationmentioning

confidence: 99%

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An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

Miliauskaitė

Kalibatiene

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Today, various stakeholders provide a large number of functionally similarWeb Services (WS) to meet increasingly complex business needs. Therefore, to distinguish similar WS, some researchers have proposed using the non-functional characteristics, named Quality of Service (QoS), and user’s needs, expressed through Quality of Experience (QoE). Thus, all those QoS and QoE attributes should be taken into account in predicting WS quality jointly, called WS QoSE (Quality of Service and Experience). However, these attributes are different in nature, i.e., QoS is data-driven and numerical, while QoE is expert-based and linguistic. Consequently, to predict WS QoSE, in this paper, we propose a hybrid fuzzy inference approach, composing both quantitative and qualitative data inputs into WS QoSE output by applying the adaptive neuro-fuzzy inference system (ANFIS). The developed prototype allows us to implement the proposed approach, investigate its performance, and study the effect of QoE attributes on WS QoSE. The results of the two experiments show good performance and suitability of the proposed hybrid fuzzy inference approach for predicting WS QoSE based on combining QoS and QoE attributes. We expect that those results inspire researchers and practitioners to understand the WS QoSE better and develop user needs matching WS.

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Section: Linguistic Approximationmentioning

confidence: 99%

“…In the literature, we can find various approaches to linguistic approximation, like the "best-fit" methods, linguistic hedges and connectives, etc. In this research, we employ the most frequently used the "best-fit" approach for linguistic approximation [42].…”

Section: Linguistic Approximationmentioning

confidence: 99%

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

Miliauskaitė

Kalibatiene

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…It is definitely an approach to consider at least as an additional source of information for informed choice in the election situation. Note that linguistic summaries are being applied more and more often recently [18,19] because of their easy understandability by laymen.…”

Section: E a Linguistic Fuzzy Modelling Interface For Decision Supportmentioning

confidence: 99%

The voter's guide to the galaxy—a multiple-criteria fuzzy decision-support tool for voters and a fresh take on election survey methods

Panzeri¹,

Stoklasová²,

Stoklasa³

2022

Annals of Computer Science and Information Systems

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This paper suggests a multiple-criteria decisionsupport tool for voters, that compares the attitudes of the voters with the declared attitudes of the political parties in several sets of relevant issues. The model intends to identify parties that seem to provide the best fit with the voter attitude-wise. The data input methodology uses discrete 5-point Likert-type scales. We investigate the effect of the inclusion of weights of different sets of issues, of the numerical anchors of the values of the Likert-type scales and also of the potential presence of extremity/leniency effect on the suggestion of the "most compatible" political party suggestion. We also propose a simple fuzzyrule based evaluation tool to identify serious incompatibilities or desired compatibilities in the attitudes of the voter and the party to the relevant issues. This tool introduces (un)acceptability thresholds for the differences in attitudes between the parties and the respondents and provides lists of parties to vote for or to avoid voting for accompanied by the strengths of these suggestions. The tool is shown to have several desirable features including lower sensitivity to small differences in the attitudes, respondents' ability to express their preferences and also preventing the compensation of unacceptable differences in some categories of important issues by high compatibility in the other categories.

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“…The research was supported by LUT research platform AMBI -Analyticsbased management for business and manufacturing industry. and is still gaining momentum [42], [43], [44], [45], [46], [47], [48], [49], [50]. Most of the methods assume at least some sort of measurability of the features of the alternatives that are being evaluated, or of the values of the criteria that are being used in the process; some circumvent the requirement of measurability by pairwise comparisons, by the use of linguistic assessments, by the use of ordinal values only etc.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Business Analytics. Selected papers of the 2021 KNOWCON-NSAIS workshop on Business Analytics

2022

Annals of Computer Science and Information Systems

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Direct material budgeting is an essential part of financial planning processes. It often implies the need to predict quantities and prices of hundreds of thousands of materials to be purchased by an enterprise in the upcoming fiscal period. Distortion effects in demand projections and overall uncertainty cause the enterprises to rely on internal data to build their forecasts.In this paper we are dealing with material demand forecasting and evaluate the feasibility of fuzzy time series forecasting models as compared to classical forecasting models. Relevant methods are shortlisted based on existing practice described in academic research. Three datasets from industry are used to evaluate the predictive performance of the shortlisted methods. Our findings show an improvement in prediction accuracy of up to 47% compared to naïve approach. Fuzzy time series models are reported to be the most reliable forecasting method for the analyzed intermittent time series in all three datasets.

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Executive summaries of uncertain values close to the gain/loss threshold – linguistic modelling perspective

Cited by 5 publications

References 43 publications

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

The voter's guide to the galaxy—a multiple-criteria fuzzy decision-support tool for voters and a fresh take on election survey methods

Recent Advances in Business Analytics. Selected papers of the 2021 KNOWCON-NSAIS workshop on Business Analytics

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