Cost-Sensitive Neural Networks and Editing Techniques for Imbalance Problems

  • R. Alejo
  • J. M. Sotoca
  • V. García
  • R. M. Valdovinos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)


The multi-class imbalance problem in supervised pattern recognition methods is receiving growing attention. Imbalanced datasets means that some classes are represented by a large number of samples while the others classes only contain a few. In real-world applications, imbalanced training sets may produce an important deterioration of the classifier performance when neural networks are applied in the classes less represented. In this paper we propose training cost-sentitive neural networks with editing techniques for handling the class imbalance problem on multi-class datasets. The aim is to remove majority samples while compensating the class imbalance during the training process. Experiments with real data sets demonstrate the effectiveness of the strategy here proposed.


Multi-class imbalance backpropagation cost function editing 


  1. 1.
    Jain, A., Mao, J., Mohiuddin, K.: Artificial neural networks: A tutorial. Computer 29(3), 31–44 (1996)CrossRefGoogle Scholar
  2. 2.
    Foody, G.: The significance of border training patterns in classification by a feedforward neural network using back propagation learning. International Journal of Remote Sensing 20(18), 3549–3562 (1999)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intelligent Data Analysis 6, 429–449 (2002)zbMATHGoogle Scholar
  5. 5.
    He, H., Garcia, E.: Learning from imbalanced data. IEEE Trans. on Knowl. and Data Eng. 21(9), 1263–1284 (2009)CrossRefGoogle Scholar
  6. 6.
    Visa, S.: Issues in mining imbalanced data sets - a review paper. In: Artificial Intelligence and Cognitive Science Conference, pp. 67–73 (2005)Google Scholar
  7. 7.
    Anand, R., Mehrotra, K., Mohan, C., Ranka, S.: Efficient classification for multiclass problems using modular neural networks. IEEE Transactions on Neural Networks 6(1), 117–124 (1995)CrossRefGoogle Scholar
  8. 8.
    Bruzzone, L., Serpico, S.: Classification of imbalanced remote-sensing data by neural networks. Pattern Recognition Letters 18, 1323–1328 (1997)CrossRefGoogle Scholar
  9. 9.
    Visa, S., Ralescu, A.: Learning imbalanced and overlapping classes using fuzzy sets. In: Workshop on Learning from Imbalanced Datasets(ICML’03), pp. 91–104 (2003)Google Scholar
  10. 10.
    Prati, R., Batista, G., Monard, M.: Class imbalances versus class overlapping: An analysis of a learning system behavior. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 312–321. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Bishop, C.M.: Neural Networks for Pattern Recognition, January 1996. Oxford University Press, USA (1996)zbMATHGoogle Scholar
  12. 12.
    Anand, R., Mehrotra, K., Mohan, C., Ranka, S.: An improved algorithm for neural network classification of imbalanced training sets. IEEE Transactions on Neural Networks 4, 962–969 (1993)CrossRefGoogle Scholar
  13. 13.
    Lawrence, S., Burns, I., Back, A., Tsoi, A., Giles, C.L.: 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
  14. 14.
    Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38(3), 257–286 (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • R. Alejo
    • 1
  • J. M. Sotoca
    • 1
  • V. García
    • 1
  • R. M. Valdovinos
    • 2
  1. 1.Institute of New Imaging Technologies Dept. Llenguatges i Sistemes InformáticsUniversitat Jaume ICastelló de la PlanaSpain
  2. 2.Centro Universitario UAEM Valle de ChalcoUniversidad Autónoma del Estado de MéxicoValle de ChalcoMexico

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