Boosting neural networks in real world applications: An empirical study

  • Hongxing He
  • Zhexue Huang
Machine Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1342)


Boosting techniques allow the combination of a collection of sequentially trained neural networks into an ensemble whose classification performance is superior to any of the individual neural networks. Empirical studies on the performance of boosting neural networks in optical character recognition have demonstrated significant improvements in classification. In this paper we report on an empirical study of boosting neural networks for classifying business data from real world databases. These data often contain noise and subjective or even contradictory classifications. Therefore, classification of such business data is a hard problem in practical applications. Two boosting algorithms were tested in this empirical study. The experimental results have shown that boosting neural networks indeed improved the classification performance. With one data set, we have achieved to date the best classification result, which had never been achieved using single and committee neural networks.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Schapire, R. E. (1990) “The Strength of Weak Learnability.” Machine Learning, vol. 5, pp. 197–227.Google Scholar
  2. [2]
    Freund, Y. and Schapire, R. E. (1995) “A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting” AT&T Bell Lab.Google Scholar
  3. [3]
    Breiman, L. (1996) “Bias, Variance, and Arcing Classifiers.” TR-460, Department of Statistics, Univ. of California, Berkeley, CA, USA.Google Scholar
  4. [4]
    Quinlan, J. R. (1996) “Boosting First-Order Learning.” In Proceedings of ALT'96, Lecture Notes in Artificial Intelligence 1160, Springer, pp. 143–155.Google Scholar
  5. [5]
    Drucker, H. and Cortes, C. (1995) “Boosting Decision Tress.” AT&T Bell Lab.Google Scholar
  6. [6]
    Drucker, H., Schapire, R. E. and Simard, P. (1993) “Boosting Performance in Neural Networks.” International Journal of Pattern Recognition and Artificial Intelligence, Vol. 7, No. 4, pp. 705–719.CrossRefGoogle Scholar
  7. [7]
    Luan F., He H. and Graco, W. (1995) “A Comparison of a Number of Supervised-Learning Techniques for Classifying a Sample of General Practitioners' Practice Profiles.” Application Stream Proceedings of Eighth Australian Joint Artificial Intelligence Conference, Canberra, Australia, pp.114–133.Google Scholar
  8. [8]
    He H. (1996 )”The Multiple Classifier Approach to a Medical Fraud Detection Problem.” Proceedings of Fourth International Conference on Control, Automation, Robotics and Vision, Singapore, pp. 241–244.Google Scholar
  9. [9]
    He H., Wang J. and Graco W. (1997) “Application of Neural Networks in Medical Fraud Detection.” Singapore International Conference on Intelligent Systems, Singapore, pp. 499–506.Google Scholar
  10. [10]
    Breiman, L. (1994) “Bagging Predictors.” TR-421, Department of Statistics, Univ. of California, Berkeley, CA, USA.Google Scholar
  11. [11]
    Haykin, S. (1994) Neural Networks, Macmilan Publishing Company.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Hongxing He
    • 1
  • Zhexue Huang
    • 2
  1. 1.Research & Analysis SectionHealth Insurance CommissionTuggeranongAustralia
  2. 2.Cooperative Research Center for Advanced Computational SystemsCSIRO Mathematical and Information SciencesCanberraAustralia

Personalised recommendations