A Proposal of Evolutionary Prototype Selection for Class Imbalance Problems

  • Salvador García
  • José Ramón Cano
  • Alberto Fernández
  • Francisco Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Unbalanced data in a classification problem appears when there are many more instances of some classes than others. Several solutions were proposed to solve this problem at data level by under-sampling. The aim of this work is to propose evolutionary prototype selection algorithms that tackle the problem of unbalanced data by using a new fitness function. The results obtained show that a balancing of data performed by evolutionary under-sampling outperforms previously proposed under-sampling methods in classification accuracy, obtaining reduced subsets and getting a good balance on data.


Geometric Mean Class Distribution Minority Class Balance Accuracy Class Imbalance Problem 
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  1. Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations 6, 1–6 (2004)CrossRefGoogle Scholar
  2. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence and Research 16, 321–357 (2002)MATHGoogle Scholar
  3. Tan, S.: Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Systems with Applications 28, 667–671 (2005)CrossRefGoogle Scholar
  4. Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl. 6, 20–29 (2004)CrossRefGoogle Scholar
  5. Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38, 257–286 (2000)MATHCrossRefGoogle Scholar
  6. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)MATHGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. Eshelman, L.J.: The CHC adaptative search algorithm: How to safe search when engaging in nontraditional genetic recombination. In: FOGA, pp. 265–283 (1990)Google Scholar
  9. Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical report, Pittsburgh, PA, USA (1994)Google Scholar
  10. Tomek, I.: Two modifications of CNN. IEEE Transactions on Systems, Man, and Communications 6, 769–772 (1976)MATHCrossRefMathSciNetGoogle Scholar
  11. Hart, P.E.: The condensed nearest neighbour rule. IEEE Transactions on Information Theory 18, 515–516 (1968)CrossRefGoogle Scholar
  12. Kubat, M., Matwin, S.: Addressing the course of imbalanced training sets: Onesided selection. In: ICML, pp. 179–186 (1997)Google Scholar
  13. Laurikkala, J.: Improving identification of difficult small classes by balancing class distribution. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS (LNAI), vol. 2101, pp. 63–66. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man and Cybernetics 2, 408–421 (1972)MATHCrossRefGoogle Scholar
  15. Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 7, 37–66 (1991)Google Scholar
  16. Barandela, R., Sánchez, J.S., García, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36, 849–851 (2003)CrossRefGoogle Scholar
  17. Newman, D.J., Hettich, S., Merz, C.B.: UCI repository of machine learning databases (1998) Google Scholar
  18. Demśar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)Google Scholar
  19. Wilcoxon, F.: Individual comparisons by rankings methods. Biometrics 1, 80–83 (1945)CrossRefGoogle Scholar
  20. Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (1997)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Salvador García
    • 1
  • José Ramón Cano
    • 2
  • Alberto Fernández
    • 1
  • Francisco Herrera
    • 1
  1. 1.Department of Computer Science and Artificial Intelligence, E.T.S.I. InformáticaUniversity of GranadaGranadaSpain
  2. 2.Department of Computer ScienceUniversity of JaénLinares, JaénSpain

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