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Combining One-Class Classifiers for Robust Novelty Detection in Gene Expression Data

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Advances in Bioinformatics and Computational Biology (BSB 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3594))

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Abstract

One-class classification techniques are able to, based only on examples of a normal profile, induce a classifier that is capable of identifying novel classes or profile changes. However, the performance of different novelty detection approaches may depend on the domain considered. This paper applies combined one-class classifiers to detect novelty in gene expression data. Results indicate that the robustness of the classification is increased with this combined approach.

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Authors and Affiliations

  1. Instituto de Ciências Matemáticas e de Computação (ICMC), Universidade de São Paulo (USP), Av. do Trabalhador São-Carlense, 400, São Carlos, 13560-970, Brasil

    Eduardo J. Spinosa & André C. P. L. F. de Carvalho

Authors
  1. Eduardo J. Spinosa
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  2. André C. P. L. F. de Carvalho
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Editors and Affiliations

  1. Department of Computer Science, Virginia Bioinformatics Institute and Virginia Polytechnic Institute and State University, Bioinformatics 1, Box 0477, 24060-0477, Blacksburg, VA, USA

    João Carlos Setubal

  2. Instituto de Quimica, Departamento de Bioquimica, Universidade de Sao Paulo, Av. Prof. Lineu Prestes 748, 05508-000, Sao Paulo, SP, Brazil

    Sergio Verjovski-Almeida

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© 2005 Springer-Verlag Berlin Heidelberg

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Spinosa, E.J., de Carvalho, A.C.P.L.F. (2005). Combining One-Class Classifiers for Robust Novelty Detection in Gene Expression Data. In: Setubal, J.C., Verjovski-Almeida, S. (eds) Advances in Bioinformatics and Computational Biology. BSB 2005. Lecture Notes in Computer Science(), vol 3594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11532323_7

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  • DOI: https://doi.org/10.1007/11532323_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28008-8

  • Online ISBN: 978-3-540-31861-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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