Hierarchy supervised SOM neural network applied for classification problem

Abstract

In this paper, supervised SOM neural network was suggested, with S-SOM and S-SOM⁺ applied for classification problems. These networks were developed from supervised and unsupervised SOM model by Kohonen and other researchers. Hierarchy supervised SOM models were developed from the S-SOM and S-SOM⁺, called HS-SOM and HS-SOM⁺. Our improvement was inspired by the idea of finding neurons that wrongly classify samples, which created extra training branches for the representative samples of these neurons. Experiments on 11 single-label classification datasets were executed. The results showed that the suggested model classified samples with high accuracy, from 92% to 100%.