Semi-supervised GSOM integrated with extreme learning machine

Semi-supervised learning with a growing self-organizing map (GSOM) is commonly used to cope with the machine learning problems. The performance of semi-supervised GSOM is associated with the structure of clustering layer, the activation level, and the weights of a classifier. Current methods have been advocated to calibrate the GSOM parameters based on local point approach. The local point approach is associated with structure of dataset. On the other hand, the semi-supervised GSOM output is so closely intertwined with problem inputs. This paper present an analytical semi-supervised learning method based on GSOM and extreme learning machine. Extreme learning machine was used to exploit the substantial classification response. However, the learning of GSOMparameters was eliminated with use of the extreme learning machine. Furthermore, the sequential extreme learning machine was implemented to achieve an online semi-supervised GSOM for streaming dataset. This study showed the proposed method converges to optimum response regardless to structure of dataset. The proposed method was applied on the online and partially labeled dataset. Online semi-supervised GSOM integrated with extreme learning machine achievement implies that the F-measure of proposed method is more precise than the conventional semi-supervised GSOM.