A hybrid SOM-kMER model for data visualization and classification

Authors:
Chee Siong Teh;Chee Peng Lim
Affiliations:
Sch. of Elec. and Electr. Eng., Univ. of Sci. Malaysia, Eng. Campus, Penang, Malaysia and Fac. of Cog. Sci. and Human Dev., Univ. Malaysia Sarawak, Sarawak, Malaysia (Correspd. Tel.: +60 4 593 778 ...;School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia
Venue:
International Journal of Hybrid Intelligent Systems
Year:
2005

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Abstract

This paper proposes a novel model that integrates the SOM (Self-Organizing Map) neural network and the kMER (kernel-based Maximum Entropy learning Rule) algorithm for data visualization and classification. The rationales and algorithm development of SOM-kMER are elaborated. Applicability of the proposed model is evaluated using a number of simulated and benchmark data sets. The outcomes demonstrate that SOM-kMER is able to achieve a faster convergence rate (as compared with the kMER) and produce visualization with fewer dead units (as compared with the SOM). The proposed SOM-kMER model is also able to form an equiprobabilistic map at the end of its learning process. On benchmark experiments, SOM-kMER achieves favourable results as compared with the SOM and other machine learning algorithms.