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Soft Computing

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01.01.2015 | Methodologies and Application

Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction

verfasst von: Masoomeh Moharrer, Hooman Tahayori, Lorenzo Livi, Alireza Sadeghian, Antonello Rizzi

Erschienen in: Soft Computing | Ausgabe 1/2015

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Abstract

In this paper, we propose a novel two-phase methodology based on interval type-2 fuzzy sets (T2FSs) to model the human perceptions of the linguistic terms used to describe the online services satisfaction. In the first phase, a type-1 fuzzy set (T1FS) model of an individual’s perception of the terms used in rating user satisfaction is derived through a decomposition-based procedure. The analysis is carried out by using well-established metrics and results from the Social Sciences context. In the second phase, interval T2FS models of online user satisfaction are calculated using a similarity-based data mining procedure. The procedure selects an essential and informative subset of the initial T1FSs that is used to discard the outliers automatically. Resulting interval T2FSs, which are synthesized based on the selected subset of T1FSs only, exhibit reasonable shapes and interpretability.

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Titel: Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction
verfasst von: Masoomeh Moharrer
Hooman Tahayori
Lorenzo Livi
Alireza Sadeghian
Antonello Rizzi
Publikationsdatum: 01.01.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 1/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1246-4

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