Robustifying OWA Operators for Aggregating Data With Outliers

We propose a version of ordered weighted averaging (OWA) operators, which are robust against inputs with outliers. Outliers may heavily bias the outputs of the standard OWA. The penalty-based method proposed here comprises both outlier detection and reallocation of weights of the OWA. At the first s...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2018-08, Vol.26 (4), p.1823-1832
Main Authors: Beliakov, Gleb, James, Simon, Wilkin, Tim, Calvo, Tomasa
Format: Article
Language:English
Subjects:
Aggregation operators
averaging
Computer simulation
Criteria
Data analysis
Electronic mail
Fuzzy systems
Open wireless architecture
Operators
ordered weighted averaging (OWA)
outliers
Outliers (statistics)
Robustness
Robustness (mathematics)
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Summary:We propose a version of ordered weighted averaging (OWA) operators, which are robust against inputs with outliers. Outliers may heavily bias the outputs of the standard OWA. The penalty-based method proposed here comprises both outlier detection and reallocation of weights of the OWA. At the first stage, the outliers are identified based on a robust criterion that can accommodate up to half the inputs being outliers, but at the same time not removing the inputs unnecessarily. Three numerical algorithms for calculating the optimal value of this criterion are proposed. At the second stage, the OWA weights are recalculated for a subset of clean data while preserving the overall character of the weighting vector. The method is numerically tested on simulated data and exemplified on aggregating a large number of online ratings where the outliers represent biased, missing, or erroneous evaluations.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2017.2752861