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

• Κύριος συγγραφέας: Pham, Thuy T. (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Springer Theses, Recognizing Outstanding Ph.D. Research,
• Θέματα: Biomedical engineering.; Data mining.; Computational intelligence.; Bioinformatics.; Biomedical Engineering and Bioengineering.; Data Mining and Knowledge Discovery.; Computational Intelligence.
• Φυσική περιγραφή: XV, 107 p. 35 illus., 32 illus. in color. online resource.
• ISBN: 9783319986753
• ISSN: 2190-5053
• DOI: 10.1007/978-3-319-98675-3

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.