Information-theoretic feature selection for the classification of hysteresis curves
(2007) International Work-Conference on Artificial Neural Networks (IWANN′07) — Location: San Sebastian (Spain) (20.June.2007)
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- Authors
- Abstract
- This paper presents a methodology for functional data analysis. It consists in extracting a large number of features with maximal content of information and then selecting the appropriate ones through a Mutual Information criterion; next, this reduced set of features is used to build a classifier. The methodology is applied to an industrial problem: the classification of the dynamic properties of elastomeric material characterized by rigidity and hysteresis curves.
- Affiliations
Universidad Carlos III de Madrid Department of Signal Theory and communications Manufacture Française des Pneumatiques Michelin
Citations
Gomez-Verdejo, V., Verleysen, M., & Fleury, J. (2007). Information-theoretic feature selection for the classification of hysteresis curves. In Francisco Sandoval et al. eds. (ed.), International Work-Conference on Artificial Neural Networks (IWANN ’07) (pp. 522-529). Springer-Verlag. https://hdl.handle.net/2078.5/254154