Feature Relevance Bounds for Ordinal Regression

Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl*, Peter Tino, Barbara Hammer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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.
Original languageEnglish
Title of host publication2019 International Conference on Document Analysis and Recognition (ICDAR)
ISBN (Print)978-1-7281-3014-9
Publication statusPublished - 20-Feb-2019


  • cs.LG
  • stat.ML

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