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Quality in crowdsourced experience-based evaluations : handling subjective responses

(2018)
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Abstract
Experience-based evaluations (XBEs) are appraisals based on what someone has understood or learned about a topic by experience. Although XBEs can be highly subjective, imprecise, and diverse, information extracted from them can result in significant benefits for companies and organizations. However, handling XBEs can entail several challenges especially when potential data quality issues, such as a lack of reliability on XBEs provided by a large and heterogeneous group of (anonymous) sources, need to be handled. In this dissertation, challenges connected with the characterization, processing and quality of XBEs have been handled. Thereby, it is studied if and how existing and novel concepts and methods in the area of computational intelligence can be used to characterize and process XBEs in such a way that one can adequately handle data quality issues on subjective data provided by a large and heterogeneous group of respondents. It has been shown that existing and novel concepts and methods connected to fuzzy set theory, which aims to find approximate, achievable and robust solutions, can be used to address these challenges. Among the novel proposed concepts, augmented appraisal degrees and augmented (Atanassov) intuitionistic fuzzy sets are deemed to be the most important contributions of this dissertation.
Keywords
Augmented Computation, Crowdsourcing, Subjective Data, Crowdsourced Data, Augmented Appraisal Degrees, Augmented (Atanassov) Intuitionistic Fuzzy Sets

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Citation

Please use this url to cite or link to this publication:

Chicago
Loor Romero, Marcelo. 2018. “Quality in Crowdsourced Experience-based Evaluations : Handling Subjective Responses”. Ghent University. Faculty of Engineering and Architecture.
APA
Loor Romero, M. (2018). Quality in crowdsourced experience-based evaluations : handling subjective responses. Ghent University. Faculty of Engineering and Architecture.
Vancouver
1.
Loor Romero M. Quality in crowdsourced experience-based evaluations : handling subjective responses. Ghent University. Faculty of Engineering and Architecture; 2018.
MLA
Loor Romero, Marcelo. “Quality in Crowdsourced Experience-based Evaluations : Handling Subjective Responses.” 2018 : n. pag. Print.
@phdthesis{8562005,
  abstract     = {Experience-based evaluations (XBEs) are appraisals based on what someone has understood or learned about a topic by experience. Although XBEs can be highly subjective, imprecise, and diverse, information extracted from them can result in significant benefits for companies and organizations. However, handling XBEs can entail several challenges especially when potential data quality issues, such as a lack of reliability on XBEs provided by a large and heterogeneous group of (anonymous) sources, need to be handled. In this dissertation, challenges connected with the characterization, processing and quality of XBEs have been handled. Thereby, it is studied if and how existing and novel concepts and methods in the area of computational intelligence can be used to characterize and process XBEs in such a way that one can adequately handle data quality issues on subjective data provided by a large and heterogeneous group of respondents. It has been shown that existing and novel concepts and methods connected to fuzzy set theory, which aims to find approximate, achievable and robust solutions, can be used to address these challenges. Among the novel proposed concepts, augmented appraisal degrees and augmented (Atanassov) intuitionistic fuzzy sets are deemed to be the most important contributions of this dissertation. },
  author       = {Loor Romero, Marcelo Eduardo},
  isbn         = {978-94-6355-106-9},
  keyword      = {Augmented Computation,Crowdsourcing,Subjective Data,Crowdsourced Data,Augmented Appraisal Degrees,Augmented (Atanassov) Intuitionistic Fuzzy Sets},
  language     = {eng},
  publisher    = {Ghent University. Faculty of Engineering and Architecture},
  school       = {Ghent University},
  title        = {Quality in crowdsourced experience-based evaluations : handling subjective responses},
  year         = {2018},
}