Self-Organizing Maps and Principal Component Analysis to Improve Classification Accuracy

Abstract

The aim of this study is to perform the Kohonen Self-Organizing Map (SOM) using Principal Component Analysis (PCA). SOM is an algorithm commonly used to visualize and classify datasets, due to its ability to project large data into a smaller dimension. However, their performance decreases when the size of the problem becomes too big. Therefore, reducing the size of the data by removing irrelevant or redundant variables and selecting only the most significant ones according to certain criteria has become a requirement before any classification, this reduction should give the best performance according to a certain objective function. Many researchers have tried to solve this problem. This study presents a new approach to improve SOM based on PCA. The experimental analysis of real data from the UCI machine learning repository shows an improvement of the proposed SOM compared to a traditional approach. More than 2% of the improvement in the accuracy of the classification is observed.

Comparative study of performing features applied in CNN architectures

“…To evaluate proposed approach, we are used different key performance measures such as accuracy, precision, recall, sensitivity and specificity [13], [14]. Mathematically, these measures are calculated as,…”