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.
The work of V. Gómez-Verdejo was supported by CM Pjt. S-0505/TIC/0223.
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Gómez-Verdejo, V., Verleysen, M., Fleury, J. (2007). Information-Theoretic Feature Selection for the Classification of Hysteresis Curves. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_64
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DOI: https://doi.org/10.1007/978-3-540-73007-1_64
Publisher Name: Springer, Berlin, Heidelberg
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