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Functional Networks and Analysis of Variance for Feature Selection

  • Conference paper
Intelligent Data Engineering and Automated Learning – IDEAL 2006 (IDEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

Abstract

In this paper a method for feature selection based on analysis of variance and using functional networks as induction algorithm is presented. It follows a backward selection search, but several features are discarded in the same step. The method proposed is compared with two SVM based methods, obtaining a smaller set of features with a similar accuracy.

This work has been partially funded by the Spanish Ministry of Science and Technology under project TIC-2003-00600 with FEDER funds.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of A Coruña, 15071, A Coruña, Spain

    Noelia Sánchez-Maroño, María Caamaño-Fernández & Amparo Alonso-Betanzos

  2. Department of Applied Mathematics and Computer Science, University of Cantabria, 39005, Santander, Spain

    Enrique Castillo

Authors
  1. Noelia Sánchez-Maroño
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  2. María Caamaño-Fernández
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  3. Enrique Castillo
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  4. Amparo Alonso-Betanzos
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera, Valencia, Spain

    Vicente Botti

  4. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

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© 2006 Springer-Verlag Berlin Heidelberg

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Sánchez-Maroño, N., Caamaño-Fernández, M., Castillo, E., Alonso-Betanzos, A. (2006). Functional Networks and Analysis of Variance for Feature Selection. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_123

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  • DOI: https://doi.org/10.1007/11875581_123

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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