Hierarchy supervised SOM neural network applied for classification problem
Keywords:Self-organizing map, supervised learning, clustering, classification, Kohonen, neural network
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%.
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