Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
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Beschreibung
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.
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.
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
Autor*in
Thuy T. Pham
Themen in »Applying Machine Learning for Automated Classification of Biomedical Data 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