Bi-objective Genetic Algorithm for Feature Selection in Ensemble Systems

  • Laura E. A. Santana
  • Anne M. P. Canuto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7552)


This paper presents the use of a bi-objective genetic algorithm to select attributes for an ensemble system. This is achieved by using this technique to simultaneously maximize the individual diversity of the base classifiers and the group diversity of an ensemble system. In order to evaluate the possible solutions obtained by this technique, two filter-based evaluation criteria will be used. Filter-based criteria were chosen because they are independent of the learning algorithm and have a low computational cost.


Ensemble systems Genetic Algorithms Feature Selection 


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  1. 1.
    Yu, S.: Feature selection and classifier ensembles: A study on hyperspectral remote sensing data. PhD thesis. University of Antwerp, Antwerp (2003)Google Scholar
  2. 2.
    Huang, C., Wang, C.: A ga-based feature selection and parameters optimizationfor support vector machines. Expert Systems with App. 31(2), 231–240 (2006)CrossRefGoogle Scholar
  3. 3.
    Opitz, D.W.: Feature selection for ensembles. In: Conference on Artificial Intelligence, AAAI. American Association for Artificial Intelligence, pp. 379–384 (1999)Google Scholar
  4. 4.
    Canuto, A.M.P., Abreu, M.C.C., de Melo Oliveira, L., Xavier Jr., J.C., de M. Santos, A.: Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recognition Letters 28(4), 472–486 (2007)CrossRefGoogle Scholar
  5. 5.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley Interscience (2004)Google Scholar
  6. 6.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010),
  8. 8.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  9. 9.
    Durillo, J., Nebro, A., Luna, F., Dorronsoro, B., Alba, E.: jmetal: a java framework for developing multi-objective optimization metaheuristics. Department of Languages and Computer Sciences, Tech. Rep. (2006)Google Scholar
  10. 10.
    Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm. Department of Electrical Engineering, Swiss Federal Institute of Technology, Tech. Rep. (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Laura E. A. Santana
    • 1
  • Anne M. P. Canuto
    • 1
  1. 1.Informatics and Applied Mathematics DepartmentFederal University of RN NatalBrazil

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