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Robustifying OWA operators for aggregating data with outliers
journal contribution
posted on 2018-08-01, 00:00 authored by Gleb BeliakovGleb Beliakov, Simon JamesSimon James, Tim WilkinTim Wilkin, T CalvoWe 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.
History
Journal
IEEE Transactions on Fuzzy SystemsVolume
26Issue
4Pagination
1823 - 1832Publisher
Institute of Electrical and Electronics Engineers (IEEE)Location
Piscataway, N.J.Publisher DOI
ISSN
1063-6706Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, IEEEUsage metrics
Keywords
aggregation operatorsaveragingOWAoutliersopen wireless architechturerobustnessstandardstoolsfuzzy systemselectronic mailScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringordered weighted averaging (OWA)WEAK MONOTONICITYWEIGHTSMAXIMUMArtificial Intelligence and Image Processing