Hierarchy supervised SOM neural network applied for classification problem - Tu, Le Anh; Hoan, Nguyen Quang; Thai, Le Son (Journal of Computer Science and Cybernetics, 2014)
<!--[if gte mso 9]> <![endif]--> 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%. <!--[if gte mso 9]> Normal 0 false false false VI X-NONE X-NONE <![endif]--><!--[if gte mso 9]> <![endif]--><!--[if gte mso 10]> <mce:style><! /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Arial","sans-serif"; mso-ascii-font-family:Arial; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Arial; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:VI; mso-fareast-language:EN-US;} --> <!--[endif] -->