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Selection of a relevant feature subset for induction tasks

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Foundations of Intelligent Systems (ISMIS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1609))

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Abstract

The representation of problems dealt with by machine learning systems use many features, only a few of which may be related to concept designing. Feature selection is the problem of choosing an ideally small subset of necessary features that are sufficient to describe the target concept. It is important both to speed up learning and to improve concept quality. A huge amount of work has been done to select from input data, a subset of the most relevant features. In this paper, a new algorithm of feature pre-processing for induction methods, namely induction of decision trees, and applied to symbolic objects is suggested. It selects a subset of the more relevant features, taking into account feature interaction. It is based both on the DPGoal and ODPGoal of considered variables (features). It is evaluated using three benchmark artificial domains. Then it is compared with Relief [1].

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Authors and Affiliations

  1. Laboratoire d’Automatique, Institut de Productique, CNRS-UMR 6596, 25 rue Alain Savary, 25000, Besancon, France

    Delphine Michaut & Pierre Baptiste

Authors
  1. Delphine Michaut
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  2. Pierre Baptiste
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Editor information

Zbigniew W. Raś Andrzej Skowron

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© 1999 Springer-Verlag Berlin Heidelberg

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Michaut, D., Baptiste, P. (1999). Selection of a relevant feature subset for induction tasks. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095134

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  • DOI: https://doi.org/10.1007/BFb0095134

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65965-5

  • Online ISBN: 978-3-540-48828-6

  • eBook Packages: Springer Book Archive

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