An Empirical Study for the Multi-class Imbalance Problem with Neural Networks

  • R. Alejo
  • J. M. Sotoca
  • G. A. Casañ
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

The latest research in neural networks demonstrates that the class imbalance problem is a critical factor in the classifiers performance when working with multi-class datasets. This occurs when the number of samples of some classes is much smaller compared to other classes. In this work, four different options to reduce the influence of the class imbalance problem in the neural networks are studied. These options consist of introducing several cost functions in the learning algorithm in order to improve the generalization ability of the networks and speed up the convergence process.

Keywords

Multi-class imbalance backpropagation cost function 

References

  1. 1.
    Kotsiantis, S., Pintelas, P.: Mixture of expert agents for handling imbalanced data sets. Annals of Mathematics and Computing & TeleInformatics 1(1), 46–55 (2003)Google Scholar
  2. 2.
    Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intelligent Data Analysis 6, 429–449 (2002)MATHGoogle Scholar
  3. 3.
    Anand, R., Mehrotra, K.G., Mohan, C.K., Ranka, S.: An improved algorithm for neural network classification of imbalanced training sets. IEEE Transactions on Neural Networks 4, 962–969 (1993)CrossRefGoogle Scholar
  4. 4.
    Bruzzone, L., Serpico, S.B.: Classification of imbalanced remote-sensing data by neural networks. Pattern Recognition Letters 18, 1323–1328 (1997)CrossRefGoogle Scholar
  5. 5.
    Zhou, Z.-H., Liu, X.-Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering 18, 63–77 (2006)CrossRefGoogle Scholar
  6. 6.
    Kukar, M., Kononenko, I.: Cost-sensitive learning with neural networks. In: 13th European Conference on Artificial Intelligence, pp. 445–449 (1998)Google Scholar
  7. 7.
    Looney, C.: Pattern Recognition Using Neuronal Networks - theory and algorithms for engineers and scientists, 1st edn. Oxford University Press, New York (1997)Google Scholar
  8. 8.
    Ding, C., Xiang, S.Q.: From multilayer perceptrons to radial basis function networks: a comparative study. In: IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 69–74 (2004)Google Scholar
  9. 9.
    Pao, Y.-H., Park, G.H., Sobajic, D.J.: Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2), 163–180 (1994)CrossRefGoogle Scholar
  10. 10.
    Alejo, R., García, V., Sotoca, J.M., Mollineda, R.A., Sánchez, J.S.: Improving the performance of the rbf neural networks with imbalanced samples. In: Sandoval, F., Gonzalez Prieto, A., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 162–169. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Lawrence, S., Burns, I., Back, A.D., Tsoi, A.C., Lee Giles, C.: Neural network classification and unequal prior class probabilities. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, pp. 299–314. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • R. Alejo
    • 1
    • 2
    • 3
  • J. M. Sotoca
    • 3
  • G. A. Casañ
    • 3
  1. 1.Centro Universitario UAEM Atlacomulco, Universidad Autónoma del Estado de MéxicoAtlacomulcoMéxico
  2. 2.Lab. Reconocimiento de Patrones, Instituto Tecnológico de TolucaMetepecMéxico
  3. 3.Dept. Llenguatges i Sistemes InformàticsUniversitat Jaume ICastelló de la PlanaSpain

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