Information-theoretic feature selection for the classification of hysteresis curves

  • Authors:

  • Vanessa Gómez-Verdejo;Michel Verleysen;Jérôme Fleury

  • Affiliations:

  • Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés, Madrid, Spain and DICE, Machine Learning Group, Université catholique de Louvain, Louvain-la-Ne ...;DICE, Machine Learning Group, Université catholique de Louvain, Louvain-la-Neuve, Belgium;Manufacture Française des Pneumatiques Michelin, Clermont, Ferrand

  • Venue:
  • IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
  • Year:
  • 2007

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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.