Ordinal regression problems are those machine learning problems where the objective is to
classify patterns using a categorical scale which shows a natural order between the labels …

Ordinal regression (OR-also known as ordinal classification) has received increasing
attention in recent times, due to its importance in IR applications such as learning to rank …

Ordinal classification or ordinal regression is a classification problem in which the labels
have an ordered arrangement between them. Due to this order, alternative performance …

We present a reduction framework from ordinal ranking to binary classification. The
framework consists of three steps: extracting extended examples from the original examples …

Image ordinal estimation is to predict the ordinal label of a given image, which can be
categorized as an ordinal regression (OR) problem. Recent methods formulate an OR …

We present a methodology for non-statistical preference learning in multicriteria ranking
based on Variable Consistency Dominance-based Rough Set Approach (VC-DRSA). A finite …

Parkinson's disease is characterised by a decrease in the density of presynaptic dopamine
transporters in the striatum. Frequently, the corresponding diagnosis is performed using a …

The recent program development industries have required problem-solving abilities for
engineers, especially application developers. However, AI-based education systems to help …

Chest radiographs (CXRs) are the most widely available radiographic imaging modality
used to detect respiratory diseases that result in lung opacities. CXR reports often use non …

Ordinal regression seeks class label predictions when the penalty incurred for mistakes
increases according to an ordering over the labels. The absolute error is a canonical …