This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction
method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing
that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing
automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly
detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability
to a wider range of unsupervised machine learning applications in subject-independent settings.