Visualizing and classifying data using a hybrid intelligent system

Authors:
Chee Siong Teh;Chee Peng Lim
Affiliations:
Faculty of Cognitive Sciences and Human Development, University Malaysia Sarawak, Sarawak, Malaysia;School of Electrical and Electronic Engineering, University of Science Malaysia, Penang, Malaysia
Venue:
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2006

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Abstract

In this paper, a hybrid intelligent system that integrates the SOM (Self-Organizing Map) neural network, kMER (kernel-based Maximum Entropy learning Rule), and Probabilistic Neural Network (PNN) for data visualization and classification is proposed. The rationales of this Probabilistic SOM-kMER model are explained, and its applicability is demonstrated using two benchmark data sets. The results are analyzed and compared with those from a number of existing methods. Implication of the proposed hybrid system as a useful and usable data visualization and classification tool is discussed.