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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How to Cite
L. A. Tu, N. Q. Hoan, and L. S. Thai, “Hierarchy supervised SOM neural network applied for classification problem”, JCC, vol. 30, no. 3, pp. 278–290, Sep. 2014.
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