Modular neural network classifiers: A comparative study

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.