Outcomes with a natural order commonly occur in prediction problems and often the available input data are mixture of complex like images tabular predictors. Deep Learning (DL) models state-of-the-art for image classification tasks but frequently treat ordinal outcomes as unordered lack interpretability. In contrast, classical regression consider outcome’s yield interpretable predictor effects ...

This paper presents an ordinal regression approach to the query-by-description problem. Instead of returning a single classification, such as genre, or a list of the top N songs assumed to be relevant, this algorithm mirrors choices similar to "like", "skip", "play", and "hate" buttons seen on commercial Internet radio stations. Ordinal regression can be viewed as a hybrid between multi-class c...

in most of the multi–criteria decision–analysis (mcda) problems in which the choquet integral is used as aggregation function, the coefficients of choquet integral (capacity) are not known in advance. actually, they could be calculated by capacity definition methods. in these methods, the preference information of decision maker (dm) is used to constitute a possible solution space. the methods ...