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International Conference on Computer Analysis of Images and Patterns

CAIP 1995: Computer Analysis of Images and Patterns pp 612–617Cite as

Estimating feature discriminant power in decision tree classifiers

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 970))

Abstract

Feature Selection is an important phase in pattern recognition system design. Even though there are well established algorithms that are generally applicable, the requirement of using certain type of criteria for some practical problems makes most of the resulting methods highly inefficient. In this work, a method is proposed to rank a given set of features in the particular case of Decision Tree classifiers, using the same information generated while constructing the tree. The preliminary results obtained with both synthetic and real data confirm that the performance is comparable to that of sequential methods with much less computation.

This work has been partially supported by project P1A94-23 Fundació Caixa Castelló, and project GV-2110/94 Conselleria d'Educació i Ciència, Generalitat Valenciana.

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Author information

Authors and Affiliations

  1. Departament d'Informàtica, Universitat Jaume I, Campus Penyeta Roja, 12071, Castelló, Spain

    I. Gracia, F. Pla & P. García

  2. Dept. d'Informàtica i Electrònica, Universitat de València Dr Moliner, 50 46100, Burjassot, València, Spain

    F. J. Ferri

Authors
  1. I. Gracia
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  2. F. Pla
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  3. F. J. Ferri
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  4. P. García
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Václav Hlaváč Radim Šára

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© 1995 Springer-Verlag Berlin Heidelberg

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Gracia, I., Pla, F., Ferri, F.J., García, P. (1995). Estimating feature discriminant power in decision tree classifiers. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_353

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  • DOI: https://doi.org/10.1007/3-540-60268-2_353

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60268-2

  • Online ISBN: 978-3-540-44781-8

  • eBook Packages: Springer Book Archive

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