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Quality in Crowdsourced Experience-Based Evaluations: Handling Subjective Responses

Marcelo Loor Romero UGent (2018)
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.
Please use this url to cite or link to this publication:
author
promoter
UGent
organization
year
type
dissertation
publication status
published
keyword
Augmented Computation, Crowdsourcing, Subjective Data, Crowdsourced Data, Augmented Appraisal Degrees, Augmented (Atanassov) Intuitionistic Fuzzy Sets
publisher
Ghent University. Faculty of Engineering and Architecture
defense location
Ghent
defense date
2018-04-05 14:00
ISBN
978-94-6355-106-9
language
English
UGent publication?
yes
classification
U
copyright statement
I have transferred the copyright for this publication to the publisher
id
8562005
handle
http://hdl.handle.net/1854/LU-8562005
date created
2018-05-16 11:17:33
date last changed
2018-05-16 12:06:45
@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},
  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},
}

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.