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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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Authors and Affiliations

  1. Pattern Analysis and Machine Intelligence Lab., Systems Design Engineering Department, University of Waterloo, Canada, N2L 3G1

    Gasser Auda & Mohamed Kamel

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  1. Gasser Auda
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  2. Mohamed Kamel
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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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