An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

Authors:
Chee Siong Teh;Chee Peng Lim
Affiliations:
Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Malaysia 94300;School of Electrical and Electronic Engineering, University of Science Malaysia, Nibong Tebal, Malaysia 14300
Venue:
Neural Processing Letters
Year:
2008

Citing 13
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Abstract

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.