Abstract
In measurement systems one of the components affecting its variation is a human factor. Man - as a process operator - measures or rates the product. Since his decisions may have significant impact on the customer satisfaction, his reliability and usefulness for the measuring tasks should be evaluated. This article describes authors proposal for a new measurement system analysis methodology for unmeasurable features. In this method many features of the product are rated during one study, and the value of them can be expressed in a nominal or an ordinal (with imprecise data) measurement scale, and each of the features can be weighted (as less or more important from the customer point of view). The goal of the methodology is to gain information about raters’ ability to define the value of each feature in relation to the specification and customer requirements. The authors propose to use a similarity measure for rates assignment of product features values. The method is based on the new fuzzy similarity coefficient \(SC\), and the level of agreement of the final decisions of the raters is analyzed based on Gwet’s \(AC_{1}\) coefficient.
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References
Hamrol A (2008) Quality management with examples (in Polish). Polish Scientific Publishers PWN
Kaydos W (1998) Operational performance measurement: increasing total productivity. CRC Press, Boca Raton
AIAG Work Group (2010) Measurement systems analysis, 4th edn. Reference manual, AIAG work group, Daimler Chrysler Corporation, Ford Motor Company, General Motors Corporation
Gwet KL (2014) Handbook of inter-rater reliability: the definitive guide to measuring the extent of agreement among raters. Advanced Analytics, LLC
Diering M, Dyczkowski K, Hamrol A (2015) Estimating the level of assessments agreement in visual inspection—the problems in determining kappa coefficients (in Polish). In: Innovation in management and production engineering
Cohen J (1960) A coefficient of agreement for nominal scale. Educ Psychol Meas 20(1):37–46
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, New Jersey
Dubois D, Prade H (2000) Fundamentals of fuzzy sets, vol 7. Kluwer, New York
Wygralak M (2013) Intelligent counting under information imprecision: applications to intelligent systems and decision support. Springer, Berlin
Dyczkowski K (2007) A less cumulative algorithm of mining linguistic browsing patterns in the world wide web. In: EUSFLAT conference, pp 129–135
Stachowiak A, Żywica P, Dyczkowski K, Wójtowicz A (2015) An interval-valued fuzzy classifier based on an uncertainty-aware similarity measure. In: Intelligent systems’ 2014, pp 741–751. Springer, Berlin
Wygralak M (2003) Cardinalities of fuzzy sets. Springer, Berlin
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Diering, M., Dyczkowski, K., Hamrol, A. (2015). New Method for Assessment of Raters Agreement Based on Fuzzy Similarity. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_36
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DOI: https://doi.org/10.1007/978-3-319-19719-7_36
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