Multilevel learning in Kohonen SOM network for classification problems

Abstract

Classification is one of the most active research and application areas of neural networks. Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. Classification is the procedure of recognizing classes of patterns that occur in the environment and assigning each pattern to its relevant class. Unlike classical statistical methods, SOM does not require any preventive knowledge about the statistical distribution of the patterns in the environment. In this study, an alternative classification of self organizing neural networks, known as multilevel learning, is proposed to solve the task of pattern separation. The performance of standard SOM and multilevel SOM are evaluated with different distance or dissimilarity measures in retrieving similarity between patterns. The purpose of this analysis is to evaluate the quality of map produced by SOM learning using different distance measures in representing a given dataset. Based on the results obtained from both SOM learning methods, predictions can be made for the unknown samples. This study aims to investigate the performance of standard SOM and multilevel SOM as supervised pattern recognition method. The multilevel SOM resembles the self-organizing map (SOM) but it has several advantages over the standard SOM. Experiments present a comparison between a standard SOM and multilevel SOM for classification of pattern for five different datasets. The results show that the multilevel SOM learning gives good classification rate, however the computational times is increased compared over the standard SOM especially for medium and large scale dataset.