Leseprobe
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Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
Thuy T. Pham
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Springer International Publishing 
Naturwissenschaften, Medizin, Informatik, Technik / Sonstiges
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.
Kundenbewertungen
Automated Feature Selection, Unsupervised Classification of Biomedical Data, Novelty Detection, Improving Classification Performance, Voting Process for Feature Selection, Anomaly Score Based Detector, Anomaly Detection for Biomedical Data, Feature Selection Based on Voting, Subject-independent Classifiers, Unsupervised Multi-class Sorting, Fog Detection Systems, Unsupervised Spike Sorting, Respiratory Artifact Detection, Unsupervised Artifact Detection, Learning for Detecting Freezing of Gait Events, Forced Oscillation Measurements, Unsupervised Anomaly Detection