Multi-class classification in the presence of labelling errors.
Jakramate Bootkrajang, Ata Kabán · 2011
Abstract. Learning a classifier from a training set that contains labelling errors is a difficult, yet not very well studied problem. Here we present a model-based approach that extends multi-class quadratic normal discriminant analysis with a model of the mislabelling process. We demonstrate the benefits of this approach in terms of parameter recovery as well as improved classification performance, on both synthetic and real-world multiclass problems. We also obtain enhanced accuracy in comparison with a previous model-free approach. 1