Multi-map Self-Organizing Maps and its application to classification problem

Abstract

In this paper, we propose a multiple-SOM's (self organizing maps) approach for classification tasks. The proposed approach generates n versions of learning data set by processing an original learning data set. A map is trained using each of the versions, and hence n maps are obtained. Each map brings a result of classification. A global classification is made by means of majority decision of such results. This scheme is applied to defecting confusion between blood samples of different patients. A GA (genetic algorithm) is employed to processing original learning data. Experimental results show that the proposed scheme achieves higher accuracy of confusion defection, compared to the case of detection made by a single map.