Hierarchy supervised SOM neural network applied for classification problem

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%.


Author Biographies

Le Anh Tu, Trường ĐH Công nghệ thông tin và truyền thông - ĐH Thái Nguyên

Nguyen Quang Hoan

Le Son Thai

Downloads

Published

2014-09-24

Issue

Section

Computer Science