The FUZZIEEE conference is held during our teaching time so I wasn’t sure I would be able to attend this year. Fortunately my colleague and co-author, Tim Wilkin, is able to attend and present our paper there. It is based on research lead by Gleb on robust aggregation. We have also submitted a paper on this topic to TFS. The R package used has been made available from the CRAN repository. Additional code can also be found here.
Title: Robust OWA-Based Aggregation for Data with Outliers
Authors: G. Beliakov, S. James, T. Wilkin and T. Calvo
We consider the problem of aggregating a large number of online ratings where there may be outliers, representing biased, missing or erroneous evaluations. The penalty-based method proposed comprises both outlier detection and reallocation of weights and we focus on models dependent on the relative order of inputs, i.e. based on OWA operators, however we also define the model for weighted means.