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
 
Autor*in
Herausgeber*in
Verleysen, Michel
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, 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.
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arXiv: 1902.07662

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