Information-Theoretic Feature Selection for Functional Data Classification Articles uri icon

publication date

  • October 2009

start page

  • 3580

end page

  • 3589

issue

  • 16-18

volume

  • 72

international standard serial number (ISSN)

  • 0925-2312

electronic international standard serial number (EISSN)

  • 1872-8286

abstract

  • The classification of functional or high-dimensional data requires to select a reduced subset of features among the initial set, both to help fighting the
    curse of dimensionality and to help interpreting the problem and the model. The
    mutual information criterion may be used in that context, but it suffers from
    the difficulty of its estimation through a finite set of samples. Efficient
    estimators are not designed specifically to be applied in a classification
    context, and thus suffer from further drawbacks and difficulties. This paper
    presents an estimator of mutual information that is specifically designed for
    classification tasks, including multi-class ones. It is combined to a recently
    published stopping criterion in a traditional forward feature selection
    procedure. Experiments on both traditional benchmarks and on an industrial
    functional classification problem show the added value of this
    estimator.