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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)

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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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© 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.

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  • 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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