  The increasing occurrence of ordinal data, mainly sociodemographic, led to a
renewed research interest in ordinal regression, i.e. the prediction of ordered
classes. Besides model accuracy, the interpretation of these models itself is
of high relevance, and existing approaches therefore enforce e.g. model
sparsity. For high dimensional or highly correlated data, however, this might
be misleading due to strong variable dependencies. In this contribution, we aim
for an identification of feature relevance bounds which - besides identifying
all relevant features - explicitly differentiates between strongly and weakly
relevant features.
