Feature Relevance Bounds for Linear Classification

Göpfert C, Pfannschmidt L, Hammer B (2017)
In: Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

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Conference Paper | Published | English
Abstract
Biomedical applications often aim for an identification of relevant features for a given classification task, since these carry the promise of semantic insight into the underlying process. For correlated input dimensions, feature relevances are not unique, and the identification of meaningful subtle biomarkers remains a challenge. One approach is to identify intervals for the possible relevance of given features, a problem related to all relevant feature determination. In this contribution, we address the important case of linear classifiers and we transfer the problem how to infer feature relevance bounds to a convex optimization problem. We demonstrate the superiority of the resulting technique in comparison to popular feature-relevance determination methods in several benchmarks.
Publishing Year
Conference
25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Location
Bruges
Conference Date
2017-04-26 – 2017-04-28
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Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; 2017.
Göpfert, C., Pfannschmidt, L., & Hammer, B. (2017). Feature Relevance Bounds for Linear Classification. Proceedings of the ESANN 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Göpfert, C., Pfannschmidt, L., and Hammer, B. (2017). “Feature Relevance Bounds for Linear Classification” in Proceedings of the ESANN (25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).
Göpfert, C., Pfannschmidt, L., & Hammer, B., 2017. Feature Relevance Bounds for Linear Classification. In Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
C. Göpfert, L. Pfannschmidt, and B. Hammer, “Feature Relevance Bounds for Linear Classification”, Proceedings of the ESANN, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2017.
Göpfert, C., Pfannschmidt, L., Hammer, B.: Feature Relevance Bounds for Linear Classification. Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2017).
Göpfert, Christina, Pfannschmidt, Lukas, and Hammer, Barbara. “Feature Relevance Bounds for Linear Classification”. Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2017.
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