GRASP for Instance Selection in Medical Data Sets

  • Alfonso Fernández
  • Abraham Duarte
  • Rosa Hernández
  • Ángel Sánchez
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 74)


Medical data sets consist of a huge amount of data organized in instances, where each one contains several attributes. The quality of the models obtained from a database strongly depends on the information previously stored on it. For this reason, these data sets must be preprocessed in order to have fairly information about patients. Data sets are preprocessed reducing the amount of data. For this task, we propose a GRASP algorithm with two different improvement strategies based on Tabu Search and Variable Neighborhood Search. Our procedure is able to widely reduce the original data keeping the most relevant information. Experimental results show how our GRASP is able to outperform the state of the art methods.


Local Search Memetic Algorithm Variable Neighborhood Variable Neighborhood Search Candidate List 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baluja, S.: Population-based incremental learning, Carnegie Mellon Univ. Pittsburgh, PA, CMU-CS-94-163 (1994) Google Scholar
  2. 2.
    Cano, J.R., Herrera, F., Lozano, M.: Using Evolutionary Algorithms as Instance Selection for Data Reduction in KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation~7, 561–575 (2003) Google Scholar
  3. 3.
    Eshelman, L.J.: The adaptive search algorithm: How to have safe search when engaging in non-traditional genetic recombination. In: Foundations of Genetic Algorithms-1, pp. 265–283. Morgan-Kauffman, San Francisco (1991) Google Scholar
  4. 4.
    Feo, T.A., Resende, M.: Greedy adaptive search procedures. Journal of Global Optimization~6, 109–133 (1995) Google Scholar
  5. 5.
    Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, Heidelberg (2002) Google Scholar
  6. 6.
    Garc\’{\i}a, S., Cano, J.R., Herrera, F.: A memetic algorithm for evolutionary prototype selection: A scaling up approach. Pattern Recognition~41, 2693–2709 (2008) Google Scholar
  7. 7.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997) Google Scholar
  8. 8.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006) Google Scholar
  9. 9.
    Hansen, P., Mladenov\‘{\i}c, N.: Variable Neightborhood Search. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 145–184. Kluwer, Dordrecht (2003) Google Scholar
  10. 10.
    Kuncheva, L.I.: Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recognition Letters~16, 809–814 (1995) Google Scholar
  11. 11.
    Liu, H., Motoda, H.: On issues of instance selection. Data Mining and Knowledge Discovery~6, 115–130 (2002) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alfonso Fernández
    • 1
  • Abraham Duarte
    • 1
  • Rosa Hernández
    • 1
  • Ángel Sánchez
    • 1
  1. 1.Departamento de Ciencias de la ComputaciónURJC 

Personalised recommendations