On 14th September 2021, Mengzi Tang successfully defended her PhD thesis entitled "Machine learning methods for ordinal classification with absolute and relative information" and was awarded the title of Doctor of Bioscience Engineering: Mathematical Modelling. Mengzi was supervised by Bernard de Baets (Ghent University) and Raúl Pérez Fernández (University of Oviedo, member of the UNIMODE research unit).
The doctoral research of Mengzi aimed at augmenting ordinal classification methods by exploiting additional relative information. This problem setting carries an undeniable interest in fields such as that of Food Science, where obtaining absolute information from expert panellists is a time-consuming and expensive task, yet untrained panellists are able to provide relative information at a lower cost. The results provided in her PhD thesis proved that the incorporation of relative information into ordinal classification problems leads to a boost in performance and should serve as an incentive for data scientists to start collecting relative information if available at a low cost.