Dimensionality Reduction of Hybrid Data Using Mutual Information-Based Unsupervised Feature Transformation: with Application on Intrusion Detection

Conventional dimensionality reduction methods are not applicable for hybrid data as they require the data set to be pure numerical. In this study, the mutual information (MI)-based unsupervised feature transformation (UFT) method which can transform symbolic features into numerical features without information loss was integrated with principle component analysis (PCA) for dimensionality reduction of hybrid data. The NSL-KDD data set for internet intrusion detection was used to verify this integrated UFT+PCA method. The experimental results show that, the UFT+PCA can reduce the dimension and improve the classification accuracies of hybrid data effectively.