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Modular Neural Network Classifiers: A Comparative Study

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Abstract

There is a wide variety of Modular Neural Network (MNN) classifiers in the literature. They differ according to the design of their architecture, task-decomposition scheme, learning procedure, and multi-module decision-making strategy. Meanwhile, there is a lack of comparative studies in the MNN literature. This paper compares ten MNN classifiers which give a good representation of design varieties, viz., Decoupled; Other-output; ART-BP; Hierarchical; Multiple-experts; Ensemble (majority vote); Ensemble (average vote); Merge-glue; Hierarchical Competitive Neural Net; and Cooperative Modular Neural Net. Two benchmark applications of different degree and nature of complexity are used for performance comparison, and the strength-points and drawbacks of the different networks are outlined. The aim is to help a potential user to choose an appropriate model according to the application in hand and the available computational resources.

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References

  1. Alpaydin, E.: 1993, Multiple networks for function learning, in: Int. Conf. on Neural Networks, Vol. 1, CA, USA, 1993, pp. 9–14.

    Google Scholar 

  2. Auda, G., Kamel, M., and Raafat, H.: 1995, Voting schemes for cooperative neural network classifiers, in: IEEE International Conference on Neural Networks, ICNN'95, Vol. 3, Perth, Australia, November 1995, pp. 1240–1243.

    Google Scholar 

  3. Auda, G., Kamel, M., and Raafat, H.: 1996, Modular neural network architectures for classification, in: International Conference on Neural Networks (ICNN96), Vol. 2, Washington, D.C., USA, June 3–6 1996, pp. 1279–1284.

    Google Scholar 

  4. Auda, G.: 1996, Cooperative modular neural network classifiers, PhD thesis, University of Waterloo, Systems Design Engineering Department, Canada.

    Google Scholar 

  5. Bartfai, G.: 1994, Hierarchical clustering with art neural networks, in: World Congress on Computational Intelligence, Vol. 2, Florida, USA, June 1994, pp. 940–944.

    Google Scholar 

  6. Battiti, R. and Colla, A.: 1994, Democracy in neural nets: Voting schemes for classification, Neural Networks 7(4), 691–707.

    Google Scholar 

  7. Corwin, E., Greni, S., Logar, A., and Whitehead, K.: 1994, A multi-stage neural network classifier, in: World Congress on Neural networks, Vol. 3, San Diego, USA, June, 5–9 1994, pp. 198–203.

    Google Scholar 

  8. De Bollivier, M., Gallinari, P., and Thiria, S.: 1991, Cooperation of neural nets and task decomposition, in: Int. Joint Conf. on Neural Networks, Vol. 2, Seattle, USA, 1991, pp. 573–576.

    Google Scholar 

  9. Hackbarth, H. and Mantel, J.: 1991, Modular connectionist structure for 100-word recognition, in: Int. Joint Conf. on Neural Networks, Vol. 2, Seattle, USA, 1991, pp. 845–849.

    Google Scholar 

  10. Jacobs, R.: 1990, Task decomposition through competition in a modular connectionist architecture, PhD thesis, University of Massachusets, Amherst, MA, USA.

    Google Scholar 

  11. Jacobs, R., Jordan, M., and Barto, A.: 1991, Task decomposition through competition in a modular con nectionist architecture: The what and where vision tasks, Neural Computation 3, 79–87. 2/1997; 15:06; v.7; p.12

    Google Scholar 

  12. Murphy, P. and Aha, D.: 1994, UCI repository of machine learning databases, Technical report, University of California, Irvine, USA, [http://www.ics.uci.edu/ _mlearn/MLRepository.html].

    Google Scholar 

  13. Murre, J.: 1992, Learning and Categorization in Modular Neural Networks, Harvester– Wheatcheaf.

  14. NeuralWare Inc.: 1993, Neural Works II/Plus: Software manual, NeuralWare Inc., PA, USA.

    Google Scholar 

  15. Raafat, H. and Rashwan, M.: 1993, A tree structured neural network, in: Int. Conf. on Document Analysis and Recognition ICDAR93, Japan, October 1993, pp. 939–942.

  16. Tsai, W., Tai, H., and Reynolds, A.: 1994, An art2-bp supervised neural net, in: World Congress on Neural Networks, Vol. 3, San Diego, USA, June 5–9 1994, pp. 619–624.

    Google Scholar 

  17. Waibel, A.: 1989, Modular construction of time-delay neural networks for speech recognition, Neural Computation 1, 39–46. /12/1997; 15:06; v.7; p.13

    Google Scholar 

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Auda, G., Kamel, M. Modular Neural Network Classifiers: A Comparative Study. Journal of Intelligent and Robotic Systems 21, 117–129 (1998). https://doi.org/10.1023/A:1007925203918

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  • DOI: https://doi.org/10.1023/A:1007925203918

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