Feature Relevance Bounds for Ordinal Regression

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

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

1 Citaat (Scopus)
44 Downloads (Pure)

Samenvatting

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.
Originele taal-2English
Titel2019 International Conference on Document Analysis and Recognition (ICDAR)
UitgeverijIEEE
ISBN van geprinte versie978-1-7281-3014-9
StatusPublished - 20-feb-2019

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