Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings

  • Thuy T.¬†Pham

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Thuy T. Pham
    Pages 1-3
  3. Thuy T. Pham
    Pages 5-18
  4. Thuy T. Pham
    Pages 19-25
  5. Thuy T. Pham
    Pages 97-102
  6. Back Matter
    Pages 103-107

About this book


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.


Novelty Detection Anomaly Score Based Detector Automated Feature Selection Feature Selection Based on Voting Unsupervised Anomaly Detection Unsupervised Artifact Detection Learning for Detecting Freezing of Gait Events Anomaly Detection for Biomedical Data Unsupervised Multi-class Sorting Voting Process for Feature Selection Improving Classification Performance Unsupervised Classification of Biomedical Data Subject-independent Classifiers Respiratory Artifact Detection Forced Oscillation Measurements Unsupervised Spike Sorting Fog Detection Systems

Authors and affiliations

  • Thuy T.¬†Pham
    • 1
  1. 1.School of Electrical and Information EngineeringThe University of SydneySydneyAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-98674-6
  • Online ISBN 978-3-319-98675-3
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • Buy this book on publisher's site
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