Self-Organizing Neural Grove and Its Application to Incremental Learning

  • Hirotaka Inoue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7872)


Recently, multiple classifier systems have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the most suitable base-classifiers for multiple classifier systems because of their simple settings and fast learning ability. However, the computation cost of the multiple classifier system based on SGNN increases in proportion to the numbers of SGNN. In this paper, we propose a novel pruning method for efficient classification and we call this model a self-organizing neural grove (SONG). Experiments have been conducted to compare the SONG with bagging and the SONG with boosting, the multiple classifier system based on C4.5, and support vector machine (SVM). The results show that the SONG can improve its classification accuracy as well as reducing the computation cost. Additionally, we investigate SONG’s incremental learning performance.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar
  2. 2.
    Quinlan, J.R.: Bagging, Boosting, and C4.5. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, Portland, OR, August 4-8, pp. 725–730. AAAI Press, The MIT Press (1996)Google Scholar
  3. 3.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)Google Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons Inc., New York (2000)Google Scholar
  5. 5.
    Wen, W.X., Jennings, A., Liu, H.: Learning a neural tree. In: The International Joint Conference on Neural Networks, Beijing, China, November 3-6, vol. 2, pp. 751–756 (1992)Google Scholar
  6. 6.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)CrossRefGoogle Scholar
  7. 7.
    Inoue, H., Narihisa, H.: Improving generalization ability of self-generating neural networks through ensemble averaging. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS (LNAI), vol. 1805, pp. 177–180. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Inoue, H., Narihisa, H.: Optimizing a multiple classifier system. In: Ishizuka, M., Sattar, A. (eds.) PRICAI 2002. LNCS (LNAI), vol. 2417, pp. 285–294. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Stone, M.: Cross-validation: A review. Math. Operationsforsch. Statist., Ser. Statistics 9(1), 127–139 (1978)MATHGoogle Scholar
  10. 10.
    Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)MathSciNetMATHGoogle Scholar
  11. 11.
    Freund, Y., Schapire, R.E.: Boosting: Foundations and Algorithms. MIT Press, Cambridge (2012)MATHGoogle Scholar
  12. 12.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  13. 13.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010),
  14. 14.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hirotaka Inoue
    • 1
  1. 1.Department of Electrical Engineering and Information ScienceKure National College of TechnologyKureJapan

Personalised recommendations