| Summary: | 博士 === 國立中央大學 === 企業管理學系 === 106 === With the rapid development of social network and online services, crowdsourcing data
has been used for many solutions in various fields. The preference sequences obtained
through crowdsourcing are valuable resources for ranking. However, the aggregation of
incomplete and inconsistent preferences is complicated. To address these challenges, this
research proposed a novel method termed robust crowd ranking (RCR) based on a consistent
Fuzzy C-means (CFCM) approach to increase the robustness and accessibility of aggregated
preference sequences obtained through crowdsourcing. To verify the robustness, accessibility,
and accuracy of RCR, comprehensive experiments were conducted using synthetic and real
data. The simulation results validated that the RCR outperforms Borda Count, Dodgson, IRV
and Tideman methods.
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