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Recursive ECOC for Microarray Data Classification

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Multiple Classifier Systems (MCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3541))

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Abstract

Recursive ECOC (RECOC) classifiers, effectively deals with microarray data complexity by encoding multiclass labels with codewords taken from Low Density Parity Check (LDPC) codes. Not all good LDPC codes result in good microarray data RECOC classifiers. A general scoring method for the identification of promising LDPC codes in the RECOC sense is presented.

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Author information

Authors and Affiliations

  1. Escuela de Electrónica, Universidad Nacional de Rosario, Argentina

    Elizabeth Tapia

  2. Instituto de Biología Molecular y Celular de Rosario, Suipacha 531, Rosario, Argentina

    Esteban Serra

  3. Dep. de Ingeniería de Sistemas Telemáticos, Universidad Politécnica de Madrid, España

    José Carlos González

Authors
  1. Elizabeth Tapia
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  2. Esteban Serra
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  3. José Carlos González
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Editor information

Editors and Affiliations

  1. NASA Ames Research Center, Mail Stop 269-1, 94035-1000, Moffett Field, CA, USA

    Nikunj C. Oza

  2. Signal Processing and Pattern Recognition Laboratory, Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ, USA

    Robi Polikar

  3. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK

    Josef Kittler

  4. University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

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© 2005 Springer-Verlag Berlin Heidelberg

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Tapia, E., Serra, E., González, J.C. (2005). Recursive ECOC for Microarray Data Classification. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_11

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  • DOI: https://doi.org/10.1007/11494683_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26306-7

  • Online ISBN: 978-3-540-31578-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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