Hierarchy supervised SOM neural network applied for classification problem

Le Anh Tu, Nguyen Quang Hoan, Le Son Thai
Author affiliations

Authors

  • Le Anh Tu Trường ĐH Công nghệ thông tin và truyền thông - ĐH Thái Nguyên
  • Nguyen Quang Hoan
  • Le Son Thai

DOI:

https://doi.org/10.15625/1813-9663/30/3/4080

Keywords:

Self-organizing map, supervised learning, clustering, classification, Kohonen, neural network

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%.


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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”, JCC, vol. 30, no. 3, pp. 278–290, Sep. 2014.

Issue

Section

Computer Science