A Tutorial on Principal Component Analysis for Dimensionality Reduction in Machine Learning

Anomaly detection has become a crucial technology in several application fields, mostly for network security. The classification challenge of anomaly detection using machine learning techniques on network data has been described here. Using the KDD99 dataset for network IDS, dimensionality reduction and classification techniques are investigated and assessed. For the application on network data, Principal Component Analysis for dimensionality reduction and Support Vector Machine for classification have been taken into consideration, and the results are examined.. The result shows the decrease in execution time for the classification as we reduce the dimension of the input data and also the precision and recall parameter values of the classification algorithm shows that the SVM with PCA method is more accurate as the number of misclassification decreases. Enormous data in health research is extremely interesting since data-based studies may move more quickly than hypothesis-based research, despite the fact that enormous databases are becoming common and hence challenging to interpret. Using Principal Component Analysis (PCA), one may make some datasets less dimensional. enhances interpretability while retaining most of the information. It does this by introducing fresh variables that are unrelated to one another.