Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings, 1st ed. 2019 Springer Theses Series

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.

Introduction .-  Background .- Algorithms .-  Point Anomaly Detection: Application to Freezing of Gait Monitoring .-  Collective Anomaly Detection: Application to Respiratory Artefact Removals.-  Spike Sorting: Application to Motor Unit Action Potential Discrimination .- Conclusion .

Nominated as an outstanding PhD thesis by The University of Sydney, Australia

Reports on an improved feature selection technique based on voting

Offers a comprehensive review of machine learning methods for unsupervised classification and feature selection