Advertisement

A New Pruning Technique for the Fuzzy ARTMAP Neural Network and Its Application to Medical Decision Support

  • Shahrul N.Y
  • Lakhmi Jain
  • C. P. Lim
Part of the Studies in Computational Intelligence book series (SCI, volume 199)

Abstract

This paper describes a neural network-based classification tool that can be deployed for data-based decision support tasks. In particular, the Fuzzy ARTMAP (FAM) network is investigated, and a new pruning technique is proposed. The pruning technique is implemented successively to eliminate those rarely activated nodes in the category layer of FAM. Three data sets with different characteristics are used to analyze its effectiveness. In addition, a benchmark medical problem is used to evaluate its applicability as a decision support tool for medical diagnosis. From the experiment, the pruning technique is able to improve classification performances, as compared with those of to the original FAM network, as well as other machine learning methods. More importantly, the pruning technique yields more stable performances with fewer nodes, and results in a more parsimonious FAM network for undertaking data classification and decision support tasks.

Keywords

Fuzzy ARTMAP pruning classification decision support medical diagnosis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abonyi, J., Szeifert, F.: Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognition Letter 24, 2195–2207Google Scholar
  2. 2.
    Anagnostopoulos, G.C., et al.: Reducing generalization error and category proliferation in ellipsoid ARTMAP via tunable misclassification error tolerance: boosted ellipsoid ARTMAP. In: International Joint Conference on Neural Networks, vol. 3, pp. 2650–2655 (2002)Google Scholar
  3. 3.
    Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, University of California at Irvine (cited March 15, 2008), http://www.ics.uci.edu/~mlearn/MLRepository.html
  4. 4.
    Bouchachia, A., Gabrys, B., Sahel, Z.: Overview of Some Incremental Learning Algorithms. In: IEEE International Fuzzy Systems Conference, 2007, pp. 1–6 (2007)Google Scholar
  5. 5.
    Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks 3, 698–713Google Scholar
  6. 6.
    Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy ARTMAP: An adaptive resonance architecture for incremental learning of analog maps. In: International Joint Conference on Neural Networks, vol. 3, pp. 309–314 (1992)Google Scholar
  7. 7.
    Dong, C.S.J., Loo, G.S.L.: Flexible web-based decision support system generator (FWDSSG) utilising software agents. In: 12th International Workshop on Database and Expert Systems Applications, pp. 892–897 (2001)Google Scholar
  8. 8.
    Le, Q., Anagnostopoulos, G.C., Georgiopoulos, M., Ports, K.: An experimental comparison of semi-supervised ARTMAP architectures, GCS and GNG classifiers. In: IEEE International Joint Conference on Neural Networks, 2005, vol. 5, pp. 3121–3126 (2005)Google Scholar
  9. 9.
    Sanchez, E.G., Dimitriadis, Y.A., Cano-Izquierdo, J.M., Lopez-Coronado, J.: μARTMAP: use of mutual information for category reduction in Fuzzy ARTMAP. IEEE Transactions on Neural Networks 13, 58–69Google Scholar
  10. 10.
    Verleysen, M., Bodt, E.D., Wertz, V.: UCL Neural Network Group, Université catholique de L-ouvain (Cited March 15, 2008), http://www.dice.ucl.ac.be/neural-nets/Research/Projects/ELENA/elena.htm
  11. 11.
    Vigdor, B., Lerner, B.: The Bayesian ARTMAP. IEEE Transactions on Neural Networks 18, 1628–1644Google Scholar
  12. 12.
    Wee, C.Y., Paramesran, R., Takeda, F., Tsuzuki, T., Kadota, H., Shimanouchi, S.: Classification of rice grains using Fuzzy Artmap neural network. In: Asia-Pacific Conference on Circuits and Systems, vol. 2, pp. 223–226Google Scholar
  13. 13.
    Zhong, M., Rosander, B., Georgiopoulos, M., Anagno-stopoulos, G.C., Mollaghasemi, M., Richie, S.: Experiments with Safe ARTMAP and Comparisons to Other ART Networks. In: International Joint Conference on Neural Networks, pp. 720–727 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shahrul N.Y
    • 1
  • Lakhmi Jain
    • 1
  • C. P. Lim
    • 1
    • 2
  1. 1.Knowledge-Based Intelligent Engineering Systems (KES) Centre, School of Electrical and Information EngineeringUniversity of South AustraliaAustralia
  2. 2.School of Electrical and Electronic EngineeringUniversity of Science MalaysiaMalaysia

Personalised recommendations