Feature Relevance Bounds for Ordinal Regression
Abstract
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.
Cite
@article{arxiv.1902.07662,
title = {Feature Relevance Bounds for Ordinal Regression},
author = {Lukas Pfannschmidt and Jonathan Jakob and Michael Biehl and Peter Tino and Barbara Hammer},
journal= {arXiv preprint arXiv:1902.07662},
year = {2019}
}
Comments
preprint of a paper accepted for oral presentation at the 27th European Symposium on Artificial Neural Networks (ESANN 2019)