  There exists a phenomenon that subjectivity highly lies in the daily
evaluation process. Our research primarily concentrates on a multi-person
evaluation system with anomaly detection to minimize the possible inaccuracy
that subjective assessment brings. We choose the two-stage screening method,
which consists of rough screening and score-weighted Kendall-$\tau$ Distance to
winnow out abnormal data, coupled with hypothesis testing to narrow global
discrepancy. Then we use Fuzzy Synthetic Evaluation Method(FSE) to determine
the significance of scores given by reviewers as well as their reliability,
culminating in a more impartial weight for each reviewer in the final
conclusion. The results demonstrate a clear and comprehensive ranking instead
of unilateral scores, and we get to have an efficiency in filtering out
abnormal data as well as a reasonably objective weight determination mechanism.
We can sense that through our study, people will have a chance of modifying a
multi-person evaluation system to attain both equity and a relatively superior
competitive atmosphere.

Computers and Society;Machine Learning;Applications