Affiliations: [a] Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran | [b] Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Correspondence:
[*]
Corresponding author: Ali Mehrizi, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. E-mail:[email protected]
Abstract: 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 GSOM parameters 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.
