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Classification and Characterization of Gene Expression Data with Generalized Eigenvalues

Author

Listed:
  • M. R. Guarracino

    (National Research Council)

  • S. Cuciniello

    (National Research Council)

  • P. M. Pardalos

    (University of Florida)

Abstract

In this study, we present Incremental Learning and Decremented Characterization of Regularized Generalized Eigenvalue Classification (ILDC-ReGEC), a novel algorithm to train a generalized eigenvalue classifier with a substantially smaller subset of points and features of the original data. The proposed method provides a constructive way to understand the influence of new training data on an existing classification model and the grouping of features that determine the class of samples. We show through numerical experiments that this technique has comparable accuracy with respect to other methods. Furthermore, experiments show that it is possible to obtain a classification model with about 30% of the training samples and less then 5% of the initial features. Matlab implementation of the ILDC-ReGEC algorithm is freely available from the authors.

Suggested Citation

  • M. R. Guarracino & S. Cuciniello & P. M. Pardalos, 2009. "Classification and Characterization of Gene Expression Data with Generalized Eigenvalues," Journal of Optimization Theory and Applications, Springer, vol. 141(3), pages 533-545, June.
    • Handle: RePEc:spr:joptap:v:141:y:2009:i:3:d:10.1007_s10957-008-9496-x
    DOI: 10.1007/s10957-008-9496-x
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    References listed on IDEAS

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    1. Claudio Cifarelli & Mario R. Guarracino & Onur Seref & Salvatore Cuciniello & Panos M. Pardalos, 2007. "Incremental Classification with Generalized Eigenvalues," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 205-219, September.
    2. Laura J. van 't Veer & Hongyue Dai & Marc J. van de Vijver & Yudong D. He & Augustinus A. M. Hart & Mao Mao & Hans L. Peterse & Karin van der Kooy & Matthew J. Marton & Anke T. Witteveen & George J. S, 2002. "Gene expression profiling predicts clinical outcome of breast cancer," Nature, Nature, vol. 415(6871), pages 530-536, January.
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    Cited by:

    1. Giovanni Felici & Kumar Parijat Tripathi & Daniela Evangelista & Mario Rosario Guarracino, 2017. "A mixed integer programming-based global optimization framework for analyzing gene expression data," Journal of Global Optimization, Springer, vol. 69(3), pages 727-744, November.

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