Classification of Imbalanced Data by Combining the Complementary Neural Network and SMOTE Algorithm

  • Piyasak Jeatrakul
  • Kok Wai Wong
  • Chun Che Fung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6444)


In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic Minority Over-sampling Technique (SMOTE) and Complementary Neural Network (CMTNN) to handle the problem of classifying imbalanced data. In order to demonstrate that the proposed technique can assist classification of imbalanced data, several classification algorithms have been used. They are Artificial Neural Network (ANN), k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). The benchmark data sets with various ratios between the minority class and the majority class are obtained from the University of California Irvine (UCI) machine learning repository. The results show that the proposed combination techniques can improve the performance for the class imbalance problem.


Class imbalanced problem artificial neural network complementary neural network classification misclassification analysis 


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  1. 1.
    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 Explorations Newsletter 6, 20–29 (2004)CrossRefGoogle Scholar
  2. 2.
    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, p. 63. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)zbMATHGoogle Scholar
  4. 4.
    Gu, Q., Cai, Z., Zhu, L., Huang, B.: Data mining on imbalanced data sets. In: International Conference on Advanced Computer Theory and Engineering, ICACTE 2008, pp. 1020–1024 (2008)Google Scholar
  5. 5.
    Gedeon, T.D., Wong, P.M., Harris, D.: Balancing bias and variance: Network topology and pattern set reduction techniques. In: Sandoval, F., Mira, J. (eds.) IWANN 1995. LNCS, vol. 930, pp. 551–558. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  6. 6.
    Tomek, I.: Two Modifications of CNN. IEEE Transactions on Systems, Man and Cybernetics 6, 769–772 (1976)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited Data. IEEE Transactions on Systems, Man and Cybernetics 2, 408–421 (1972)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Gedeon, T.D., Bowden, T.G.: Heuristic pattern reduction. In: International Joint Conference on Neural Networks, Beijing, vol. 2, pp. 449–453 (1992)Google Scholar
  9. 9.
    Barandela, R., Sanchez, J.S., Garcia, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36, 849–851 (2003)CrossRefGoogle Scholar
  10. 10.
    Kraipeerapun, P., Fung, C.C., Nakkrasae, S.: Porosity prediction using bagging of complementary neural networks. In: Yu, W., He, H., Zhang, N. (eds.) ISNN 2009. LNCS, vol. 5551, pp. 175–184. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Kraipeerapun, P., Fung, C.C.: Binary classification using ensemble neural networks and interval neutrosophic sets. Neurocomput. 72, 2845–2856 (2009)CrossRefGoogle Scholar
  12. 12.
    Jeatrakul, P., Wong, K.W., Fung, C.C.: Data cleaning for classification using misclassification analysis. Journal of Advanced Computational Intelligence and Intelligent Informatics 14(3), 297–302 (2010)CrossRefGoogle Scholar
  13. 13.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Piyasak Jeatrakul
    • 1
  • Kok Wai Wong
    • 1
  • Chun Che Fung
    • 1
  1. 1.School of Information TechnologyMurdoch UniversityMurdochWestern Australia

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