An artificial neural network for pattern classification and visualization - Sandra, Ong Pi Yin. (Universiti Malaysia Sarawak, UNIMAS, 2010)
The industrial revolution and the birth of computers has led to a deeper exploration of Artificial Neural Network (ANN), where scientist tries to emulate the biological neural network. Today, ANN has proven to be able to im itate the human neural network and perform task such as solving real world problems. This study aims to explore in detail an ANN model that is able to perform the task of pattern classification and visualisation , and as well as to evaluate the performance of this model. This proposed model is know as the Self - Organizing Map (SOM) or Kohonen’s Map. In order to determine it’s accuracy, the SOM classifier is tested using a few simulated Gaussian data sets and a real world data set, the Pima Indians Diabetes da ta set. The experiments conducted showed that SOM classifier is able to perform the task of classification and visualisation . However, the classifier obtained a non - impressive accuracy of 73.16% in classifying the real world problem. This results indicate that SOM is not reliable compared to other classification applications in literature and can be enhanced in terms of it’s performance.