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Classification consistency analysis for bootstrapping gene selection

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Abstract

Consistency modelling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of operations such as classification, clustering, or gene selection on a training set is often found to be very different from the same operations on a testing set, presenting a serious consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, this paper proposes a new concept of classification consistency and applies it for microarray gene selection problem using a bootstrapping approach, with encouraging results.

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Acknowledgments

The research presented in the paper was partially funded by the New Zealand Foundation for Research, Science and Technology under the grant: NERF/AUTX02-01.

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Authors and Affiliations

  1. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland, 1020, New Zealand

    Shaoning Pang, Ilkka Havukkala, Yingjie Hu & Nikola Kasabov

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  1. Shaoning Pang
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  2. Ilkka Havukkala
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  3. Yingjie Hu
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  4. Nikola Kasabov
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Correspondence to Shaoning Pang.

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Pang, S., Havukkala, I., Hu, Y. et al. Classification consistency analysis for bootstrapping gene selection. Neural Comput & Applic 16, 527–539 (2007). https://doi.org/10.1007/s00521-007-0110-1

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  • DOI: https://doi.org/10.1007/s00521-007-0110-1

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