Feature Relevance Bounds for Ordinal Regression
Pfannschmidt L, Jakob J, Biehl M, Tino P, Hammer B (2019)
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc.
Konferenzbeitrag
| Veröffentlicht | Englisch
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Herausgeber*in
Verleysen, Michel
Einrichtung
Abstract / Bemerkung
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.
Stichworte
ordinal regression;
interpretable models;
global feature relevance
Erscheinungsjahr
2019
Titel des Konferenzbandes
Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019)
Konferenz
European Symposium on Artificial Neural Networks (ESANN 2019)
Konferenzort
Bruges
Konferenzdatum
2019-04-24 – 2019-04-26
ISBN
9782875870650
Page URI
https://pub.uni-bielefeld.de/record/2933893
Zitieren
Pfannschmidt L, Jakob J, Biehl M, Tino P, Hammer B. Feature Relevance Bounds for Ordinal Regression. In: Verleysen M, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Louvain-la-Neuve: i6doc; 2019.
Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., & Hammer, B. (2019). Feature Relevance Bounds for Ordinal Regression. In M. Verleysen (Ed.), Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019) Louvain-la-Neuve: i6doc.
Pfannschmidt, Lukas, Jakob, Jonathan, Biehl, Michael, Tino, Peter, and Hammer, Barbara. 2019. “Feature Relevance Bounds for Ordinal Regression”. In Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), ed. Michel Verleysen. Louvain-la-Neuve: i6doc.
Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., and Hammer, B. (2019). “Feature Relevance Bounds for Ordinal Regression” in Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), Verleysen, M. ed. ( Louvain-la-Neuve: i6doc).
Pfannschmidt, L., et al., 2019. Feature Relevance Bounds for Ordinal Regression. In M. Verleysen, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Louvain-la-Neuve: i6doc.
L. Pfannschmidt, et al., “Feature Relevance Bounds for Ordinal Regression”, Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), M. Verleysen, ed., Louvain-la-Neuve: i6doc, 2019.
Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., Hammer, B.: Feature Relevance Bounds for Ordinal Regression. In: Verleysen, M. (ed.) Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). i6doc, Louvain-la-Neuve (2019).
Pfannschmidt, Lukas, Jakob, Jonathan, Biehl, Michael, Tino, Peter, and Hammer, Barbara. “Feature Relevance Bounds for Ordinal Regression”. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Ed. Michel Verleysen. Louvain-la-Neuve: i6doc, 2019.
Link(s) zu Volltext(en)
Access Level
Open Access
arXiv: 
Google Scholar
ISBN Suche