Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B (2020)
Neurocomputing.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Pfannschmidt, LukasUniBi ;
Jakob, JonathanUniBi;
Hinder, FabianUniBi;
Biehl, Michael;
Tino, Peter;
Hammer, BarbaraUniBi
Einrichtung
Abstract / Bemerkung
Advances in machine learning technologies have led to increasingly powerful
models in particular in the context of big data. Yet, many application
scenarios demand for robustly interpretable models rather than optimum model
accuracy; as an example, this is the case if potential biomarkers or causal
factors should be discovered based on a set of given measurements. In this
contribution, we focus on feature selection paradigms, which enable us to
uncover relevant factors of a given regularity based on a sparse model. We
focus on the important specific setting of linear ordinal regression, i.e.
data have to be ranked into one of a finite number of ordered categories by a
linear projection. Unlike previous work, we consider the case that features are
potentially redundant, such that no unique minimum set of relevant features
exists. We aim for an identification of all strongly and all weakly relevant
features as well as their type of relevance (strong or weak); we achieve this
goal by determining feature relevance bounds, which correspond to the minimum
and maximum feature relevance, respectively, if searched over all equivalent
models. In addition, we discuss how this setting enables us to substitute some
of the features, e.g. due to their semantics, and how to extend the framework
of feature relevance intervals to the setting of privileged information, i.e.
potentially relevant information is available for training purposes only, but
cannot be used for the prediction itself.
Stichworte
Global Feature Relevance;
Feature Selection;
Interpretability;
Ordinal Regression;
Privileged Information
Erscheinungsjahr
2020
Zeitschriftentitel
Neurocomputing
Urheberrecht / Lizenzen
ISSN
0925-2312
Page URI
https://pub.uni-bielefeld.de/record/2939517
Zitieren
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. 2020.
Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., & Hammer, B. (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. doi:10.1016/j.neucom.2019.12.133
Pfannschmidt, Lukas, Jakob, Jonathan, Hinder, Fabian, Biehl, Michael, Tino, Peter, and Hammer, Barbara. 2020. “Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information”. Neurocomputing.
Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., and Hammer, B. (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.
Pfannschmidt, L., et al., 2020. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.
L. Pfannschmidt, et al., “Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information”, Neurocomputing, 2020.
Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., Hammer, B.: Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. (2020).
Pfannschmidt, Lukas, Jakob, Jonathan, Hinder, Fabian, Biehl, Michael, Tino, Peter, and Hammer, Barbara. “Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information”. Neurocomputing (2020).
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Open Access
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