Hierarchy supervised SOM neural network applied for classification problem
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DOI:
https://doi.org/10.15625/1813-9663/30/3/4080Keywords:
Self-organizing map, supervised learning, clustering, classification, Kohonen, neural networkAbstract
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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Published
24-09-2014
How to Cite
[1]L. A. Tu, N. Q. Hoan, and L. S. Thai, “Hierarchy supervised SOM neural network applied for classification problem”, J. Comput. Sci. Cybern., vol. 30, no. 3, pp. 278–290, Sep. 2014.
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Computer Science
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