Recently, solving ordinal classification problems using machine learning and deep learning
techniques has acquired important attention. There are many real-world problems in …

The current ML approaches do not fully focus to answer a still unresolved and topical
challenge, namely the prediction of priorities of COVID-19 vaccine administration. Thus, our …

Ordinal classification of imbalanced data is a challenging problem that appears in many real
world applications. The challenge is to simultaneously consider the order of the classes and …

In the last years, multiple quality control tasks consist in classifying some items based on
their aesthetic characteristics (aesthetic quality control, AQC), where usually the aspect of …

Outcomes with a natural order commonly occur in prediction problems and often the
available input data are a mixture of complex data like images and tabular predictors. Deep …

Nowadays, decision support systems (DSSs) are widely used in several application
domains, from industrial to healthcare and medicine fields. Concerning the industrial …

Background Adopting a computational approach for the assessment of urine cytology
specimens has the potential to improve the efficiency, accuracy, and reliability of bladder …

Time-series classification (TSC) covers the supervised learning problem where input data is
provided in the form of series of values observed through repeated measurements over time …

Deep learning techniques for ordinal classification have recently gained significant attention.
Predicting an ordinal variable, that is, a variable that demonstrates a natural relationship …

Automatic classification of cervical cells plays a critical role in the Computer-assisted
Cytology Test (CCT) system. The efficiency of the CCT system can be promoted by …