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Interaction of individually and collectively treated neurons for explicit class structure in self-organizing maps

In this paper, we propose a new type of neural learning method where two types of neurons interact with other. The two types of neurons are individually and collectively treated neurons. Though there are many types of interaction between two neurons, we suppose for simplification that individually treated neurons should be similar to collectively treated neurons as much as possible. This model can be applied to the self-organizing maps whose performance can be enhanced by the introduction of interaction of neurons. Then, we applied the method to the breast tissue and protein classification problem of the machine learning database. The experimental results showed that much clearer class boundaries could be produced, though quantization and topographic errors were slightly higher than those by the conventional SOM.