Evolutionary Search of Thresholds for Robust Feature Set Selection: Application to the Analysis of Microarray Data

  • Carlos Cotta
  • Christian Sloper
  • Pablo Moscato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3005)


We deal with two important problems in pattern recognition that arise in the analysis of large datasets. While most feature subset selection methods use statistical techniques to preprocess the labeled datasets, these methods are generally not linked with the combinatorial properties of the final solutions. We prove that it is NP-hard to obtain an appropriate set of thresholds that will transform a given dataset into a binary instance of a robust feature subset selection problem. We address this problem using an evolutionary algorithm that learns the appropriate value of the thresholds. The empirical evaluation shows that robust subset of genes can be obtained. This evaluation is done using real data corresponding to the gene expression of lymphomas.


Evolutionary Algorithm Vertex Cover Robust Feature Greedy Heuristic Reduction Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Carlos Cotta
    • 1
  • Christian Sloper
    • 2
  • Pablo Moscato
    • 3
  1. 1.Dept. Lenguajes y Ciencias de la ComputaciónUniversity of MálagaMálagaSpain
  2. 2.Department of InformaticsUniversity of Bergen, HIBBergenNorway
  3. 3.Newcastle Bioinformatics Initiative, School of Electrical Engineering and Computer ScienceThe University of NewcastleCallaghanAustralia

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