CMNN: Cooperative Modular Neural Networks for pattern recognition

A new modular neural network model, the Cooperative Modular Neural Network (CMNN), is introduced. The main idea is to decompose the classification task by dividing the input space into several homogeneous regions (in terms of degree of overlap). A hierarchical task-decomposition algorithm is developed for this task. The structure is modularized and a separate module is trained to draw the classification decision-boundaries within specific regions. Moreover, the task-decomposition algorithm assigns some additional "specialized modules" to high overlap regions detected in the input space. CMNN is, then, experimentally compared to both the non-modular alternative, and several famous modular models, viz., Decoupled; Other-output; ART-BP; Hierarchical; Multiple-experts; Ensembles (two versions); and Merge-glue models. For a comprehensive comparison with non-modular alternatives, ten famous benchmark classification applications are used, viz., Iris; Numerals recognition; Arabic handwritten letters; Two-dimension 20-class; Heart infection; 2-class; Glass; Cleveland heart diseases; Vowels; and Hepatitis databases. Compared to the non-modular alternative, CMNN reduces the benchmark percentage of classification error significantly, or otherwise, performs as good as the non-modular network (in extreme cases of low and high overlap). Compared to previous modular designs, a significant enhancement in the efficiency of the modular structure is noticed, CMNN's trade-off, though, is a slight increase in the required computational work as compared to some models. (C) 1997 Elsevier Science B.V.