Our Robust OWA work which we started earlier last year and submitted to FUZZIEEE 2016 was accepted to IEEE Transactions on Fuzzy Systems. We also had a write-up as a Deakin Media Release. I find robust statistics and robust methods both interesting and pertinent given how blindly some machine learning methods can be applied these days. (available online)
Title: Robustifying OWA operators for aggregating data with outliers
Authors: G. Beliakov, S. James and T. Wilkin
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.